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Smith TP, Mishra S, Dorigatti I, Dixit MK, Tristem M, Pearse WD. Differential responses of SARS-CoV-2 variants to environmental drivers during their selective sweeps. Sci Rep 2024; 14:13326. [PMID: 38858479 PMCID: PMC11164892 DOI: 10.1038/s41598-024-64044-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 06/04/2024] [Indexed: 06/12/2024] Open
Abstract
Previous work has shown that environmental variables affect SARS-CoV-2 transmission, but it is unclear whether different strains show similar environmental responses. Here we leverage genetic data on the transmission of three (Alpha, Delta and Omicron BA.1) variants of SARS-CoV-2 throughout England, to unpick the roles that climate and public-health interventions play in the circulation of this virus. We find evidence for enhanced transmission of the virus in colder conditions in the first variant selective sweep (of Alpha, in winter), but limited evidence of an impact of climate in either the second (of Delta, in the summer, when vaccines were prevalent) or third sweep (of Omicron, in the winter, during a successful booster-vaccination campaign). We argue that the results for Alpha are to be expected if the impact of climate is non-linear: we find evidence of an asymptotic impact of temperature on the alpha variant transmission rate. That is, at lower temperatures, the influence of temperature on transmission is much higher than at warmer temperatures. As with the initial spread of SARS-CoV-2, however, the overwhelming majority of variation in disease transmission is explained by the intrinsic biology of the virus and public-health mitigation measures. Specifically, when vaccination rates are high, a major driver of the spread of a new variant is it's ability to evade immunity, and any climate effects are secondary (as evidenced for Delta and Omicron). Climate alone cannot describe the transmission dynamics of emerging SARS-CoV-2 variants.
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Affiliation(s)
- Thomas P Smith
- Georgina Mace Centre for the Living Planet, Department of Life Sciences, Imperial College London, Silwood Park, Ascot, Berkshire, SL5 7PY, UK.
| | - Swapnil Mishra
- Saw Swee Hock School of Public Health and Institute of Data Science, National University of Singapore and National University Health System, 12 Science Dr 2, Singapore, 117549, Singapore
| | - Ilaria Dorigatti
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, 90 Wood Lane, London, W12 OBZ, UK
| | - Mahika K Dixit
- Georgina Mace Centre for the Living Planet, Department of Life Sciences, Imperial College London, Silwood Park, Ascot, Berkshire, SL5 7PY, UK
| | - Michael Tristem
- Georgina Mace Centre for the Living Planet, Department of Life Sciences, Imperial College London, Silwood Park, Ascot, Berkshire, SL5 7PY, UK
| | - William D Pearse
- Georgina Mace Centre for the Living Planet, Department of Life Sciences, Imperial College London, Silwood Park, Ascot, Berkshire, SL5 7PY, UK
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2
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Lo YTE, Mitchell DM, Gasparrini A. Compound mortality impacts from extreme temperatures and the COVID-19 pandemic. Nat Commun 2024; 15:4289. [PMID: 38782899 PMCID: PMC11116452 DOI: 10.1038/s41467-024-48207-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Accepted: 04/24/2024] [Indexed: 05/25/2024] Open
Abstract
Extreme weather and coronavirus-type pandemics are both leading global health concerns. Until now, no study has quantified the compound health consequences of the co-occurrence of them. We estimate the mortality attributable to extreme heat and cold events, which dominate the UK health burden from weather hazards, in England and Wales in the period 2020-2022, during which the COVID-19 pandemic peaked in terms of mortality. We show that temperature-related mortality exceeded COVID-19 mortality by 8% in South West England. Combined, extreme temperatures and COVID-19 led to 19 (95% confidence interval: 16-22 in North West England) to 24 (95% confidence interval: 20-29 in Wales) excess deaths per 100,000 population during heatwaves, and 80 (95% confidence interval: 75-86 in Yorkshire and the Humber) to 127 (95% confidence interval: 123-132 in East of England) excess deaths per 100,000 population during cold snaps. These numbers are at least ~2 times higher than the previous decade. Society must increase preparedness for compound health crises such as extreme weather coinciding with pandemics.
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Affiliation(s)
- Y T Eunice Lo
- Cabot Institute for the Environment, University of Bristol, Bristol, UK.
- Elizabeth Blackwell Institute for Health Research, University of Bristol, Bristol, UK.
| | - Dann M Mitchell
- Cabot Institute for the Environment, University of Bristol, Bristol, UK
- School of Geographical Sciences, University of Bristol, Bristol, UK
| | - Antonio Gasparrini
- Environment & Health Modelling (EHM) Lab, Department of Public Health Environments and Society, London School of Hygiene & Tropical Medicine, London, UK
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Price BS, Khodaverdi M, Hendricks B, Smith GS, Kimble W, Halasz A, Guthrie S, Fraustino JD, Hodder SL. Enhanced SARS-CoV-2 case prediction using public health data and machine learning models. JAMIA Open 2024; 7:ooae014. [PMID: 38444986 PMCID: PMC10913390 DOI: 10.1093/jamiaopen/ooae014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 01/29/2024] [Accepted: 02/08/2024] [Indexed: 03/07/2024] Open
Abstract
Objectives The goal of this study is to propose and test a scalable framework for machine learning (ML) algorithms to predict near-term severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cases by incorporating and evaluating the impact of real-time dynamic public health data. Materials and Methods Data used in this study include patient-level results, procurement, and location information of all SARS-CoV-2 tests reported in West Virginia as part of their mandatory reporting system from January 2021 to March 2022. We propose a method for incorporating and comparing widely available public health metrics inside of a ML framework, specifically a long-short-term memory network, to forecast SARS-CoV-2 cases across various feature sets. Results Our approach provides better prediction of localized case counts and indicates the impact of the dynamic elements of the pandemic on predictions, such as the influence of the mixture of viral variants in the population and variable testing and vaccination rates during various eras of the pandemic. Discussion Utilizing real-time public health metrics, including estimated Rt from multiple SARS-CoV-2 variants, vaccination rates, and testing information, provided a significant increase in the accuracy of the model during the Omicron and Delta period, thus providing more precise forecasting of daily case counts at the county level. This work provides insights on the influence of various features on predictive performance in rural and non-rural areas. Conclusion Our proposed framework incorporates available public health metrics with operational data on the impact of testing, vaccination, and current viral variant mixtures in the population to provide a foundation for combining dynamic public health metrics and ML models to deliver forecasting and insights in healthcare domains. It also shows the importance of developing and deploying ML frameworks in rural settings.
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Affiliation(s)
- Bradley S Price
- Department of Management Information Systems, West Virginia University, Morgantown, WV 26505, United States
- West Virginia Clinical and Translational Science Institute, Morgantown, WV 26506, United States
| | - Maryam Khodaverdi
- West Virginia Clinical and Translational Science Institute, Morgantown, WV 26506, United States
| | - Brian Hendricks
- West Virginia Clinical and Translational Science Institute, Morgantown, WV 26506, United States
- Department of Epidemiology and Biostatistics, West Virginia University, Morgantown, WV 26505, United States
| | - Gordon S Smith
- West Virginia Clinical and Translational Science Institute, Morgantown, WV 26506, United States
- Department of Epidemiology and Biostatistics, West Virginia University, Morgantown, WV 26505, United States
| | - Wes Kimble
- West Virginia Clinical and Translational Science Institute, Morgantown, WV 26506, United States
| | - Adam Halasz
- School of Mathematics and Data Science, West Virginia University, Morgantown, WV 26506, United States
| | - Sara Guthrie
- Department of Sociology and Anthropology, West Virginia University, Morgantown, WV 26505, United States
| | - Julia D Fraustino
- Department of Strategic Communication, Reed College of Media, West Virginia University, Morgantown, WV 26505, United States
| | - Sally L Hodder
- West Virginia Clinical and Translational Science Institute, Morgantown, WV 26506, United States
- Department of Medicine, West Virginia University, Morgantown, WV 26506, United States
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Huber V, Breitner-Busch S, He C, Matthies-Wiesler F, Peters A, Schneider A. Heat-Related Mortality in the Extreme Summer of 2022. DEUTSCHES ARZTEBLATT INTERNATIONAL 2024; 121:79-85. [PMID: 38169332 PMCID: PMC11002439 DOI: 10.3238/arztebl.m2023.0254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 11/16/2023] [Accepted: 11/16/2023] [Indexed: 01/05/2024]
Abstract
BACKGROUND Estimating the excess mortality attributable to heat is a central element of the documentation of the consequences of climate change for human health. Until now, estimates of heatrelated deaths in Germany by the Robert Koch Institute (RKI) have been based on weekly mortality records. METHODS Our study is the first to use higher resolution data-i.e. daily all-cause mortality linked to daily mean temperatures-from each of the German federal states to assess the heat-related mortality from 2000 to 2023 in Germany, employing quasi-Poisson models and multivariate meta-regression analyses. We focus our analysis on the extreme summer of 2022. RESULTS Our analysis yielded an estimate of 9100 (95% CI: [7300; 10 700]) heat-related deaths in Germany for the summer of 2022, whereas previous studies of the RKI estimated the number of heatrelated deaths at 4500 [2100; 7000]. When we set a higher temperature threshold in the definition of the heat risk, we arrived at a figure of 6900 [5500; 8100] heat-related deaths in 2022. In other summers that-similarly to 2022-were characterized by large fluctuations in daily mean temperatures, we also robustly estimated higher numbers of heat-related deaths than the RKI did. The exclusion of reported deaths due to COVID-19 had only a minor effect on our estimates. CONCLUSION Our findings suggest that previous studies based on weekly mortality data have underestimated the full extent of heat-related mortality in Germany, particularly in the extreme summer of 2022. The monitoring of heat-related mortality should be systematic and as comprehensive as possible if it is to enable the development of effective heat-health action plans.
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Affiliation(s)
- Veronika Huber
- Institute of Epidemiology, The Institute for Medical Information Processing, Biometry, and Epidemiology (IBE), Medical Faculty, Ludwig-Maximilians-Universität (LMU), München, Germany
- Institute of Epidemiology, Helmholtz Center Munich – German Research Center for Environmental Health, Neuherberg, Germany
| | - Susanne Breitner-Busch
- Institute of Epidemiology, The Institute for Medical Information Processing, Biometry, and Epidemiology (IBE), Medical Faculty, Ludwig-Maximilians-Universität (LMU), München, Germany
- Institute of Epidemiology, Helmholtz Center Munich – German Research Center for Environmental Health, Neuherberg, Germany
| | - Cheng He
- Institute of Epidemiology, Helmholtz Center Munich – German Research Center for Environmental Health, Neuherberg, Germany
| | - Franziska Matthies-Wiesler
- Institute of Epidemiology, Helmholtz Center Munich – German Research Center for Environmental Health, Neuherberg, Germany
- German Alliance on Climate Change and Health (KLUG e.V.), Berlin, Germany
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Center Munich – German Research Center for Environmental Health, Neuherberg, Germany
- Munich Heart Alliance, German Center for Cardiovascular Health (DZHK e.V., partner-site Munich), München, Germany
| | - Alexandra Schneider
- Institute of Epidemiology, Helmholtz Center Munich – German Research Center for Environmental Health, Neuherberg, Germany
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Wagatsuma K. Association of Ambient Temperature and Absolute Humidity with the Effective Reproduction Number of COVID-19 in Japan. Pathogens 2023; 12:1307. [PMID: 38003771 PMCID: PMC10675148 DOI: 10.3390/pathogens12111307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 10/28/2023] [Accepted: 10/30/2023] [Indexed: 11/26/2023] Open
Abstract
This study aimed to quantify the exposure-lag-response relationship between short-term changes in ambient temperature and absolute humidity and the transmission dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Japan. The prefecture-specific daily time-series of newly confirmed cases, meteorological variables, retail and recreation mobility, and Government Stringency Index were collected for all 47 prefectures of Japan for the study period from 15 February 2020 to 15 October 2022. Generalized conditional Gamma regression models were formulated with distributed lag nonlinear models by adopting the case-time-series design to assess the independent and interactive effects of ambient temperature and absolute humidity on the relative risk (RR) of the time-varying effective reproductive number (Rt). With reference to 17.8 °C, the corresponding cumulative RRs (95% confidence interval) at a mean ambient temperatures of 5.1 °C and 27.9 °C were 1.027 (1.016-1.038) and 0.982 (0.974-0.989), respectively, whereas those at an absolute humidity of 4.2 m/g3 and 20.6 m/g3 were 1.026 (1.017-1.036) and 0.995 (0.985-1.006), respectively, with reference to 10.6 m/g3. Both extremely hot and humid conditions synergistically and slightly reduced the Rt. Our findings provide a better understanding of how meteorological drivers shape the complex heterogeneous dynamics of SARS-CoV-2 in Japan.
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Affiliation(s)
- Keita Wagatsuma
- Division of International Health (Public Health), Graduate School of Medical and Dental Sciences, Niigata University, Niigata 951-8510, Japan; ; Tel.: +81-25-227-2129
- Japan Society for the Promotion of Science, Tokyo 102-0083, Japan
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6
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Villatoro-García JA, López-Domínguez R, Martorell-Marugán J, Luna JDD, Lorente JA, Carmona-Sáez P. Exploring the interplay between climate, population immunity and SARS-CoV-2 transmission dynamics in Mediterranean countries. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 897:165487. [PMID: 37451463 DOI: 10.1016/j.scitotenv.2023.165487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 07/08/2023] [Accepted: 07/10/2023] [Indexed: 07/18/2023]
Abstract
The relationship between SARS-CoV-2 transmission and environmental factors has been analyzed in numerous studies since the outbreak of the pandemic, resulting in heterogeneous results and conclusions. This may be due to differences in methodology, considered variables, confounding factors, studied periods and/or lack of adequate data. Furthermore, previous works have reported that the lack of population immunity is the fundamental driver in transmission dynamics and can mask the potential impact of environmental variables. In this study, we aimed to investigate the association between climate variables and COVID-19 transmission considering the influence of population immunity. We analyzed two different periods characterized by the absence of vaccination (low population immunity) and a high degree of vaccination (high level of population immunity), respectively. Although this study has some limitations, such us the restriction to a specific climatic zone and the omission of other environmental factors, our results indicate that transmission of SARS-CoV-2 may increase independently of temperature and specific humidity in periods with low levels of population immunity while a negative association is found under conditions with higher levels of population immunity in the analyzed regions.
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Affiliation(s)
- Juan Antonio Villatoro-García
- Department of Statistics and Operations Research, University of Granada, Granada, Spain; GENYO. Centre for Genomics and Oncological Research: Pfizer / University of Granada / Andalusian Regional Government, PTS Granada, 18016 Granada, Spain
| | - Raúl López-Domínguez
- Department of Statistics and Operations Research, University of Granada, Granada, Spain; GENYO. Centre for Genomics and Oncological Research: Pfizer / University of Granada / Andalusian Regional Government, PTS Granada, 18016 Granada, Spain
| | - Jordi Martorell-Marugán
- GENYO. Centre for Genomics and Oncological Research: Pfizer / University of Granada / Andalusian Regional Government, PTS Granada, 18016 Granada, Spain; Fundación para la Investigación Biosanitaria de Andalucía Oriental-Alejandro Otero (FIBAO), Spain
| | - Juan de Dios Luna
- Department of Statistics and Operations Research, University of Granada, Granada, Spain
| | - José Antonio Lorente
- GENYO. Centre for Genomics and Oncological Research: Pfizer / University of Granada / Andalusian Regional Government, PTS Granada, 18016 Granada, Spain; Department of Legal Medicine and Toxicology, Faculty of Medicine, University of Granada, PTS Granada, 18016 Granada, Spain
| | - Pedro Carmona-Sáez
- Department of Statistics and Operations Research, University of Granada, Granada, Spain; GENYO. Centre for Genomics and Oncological Research: Pfizer / University of Granada / Andalusian Regional Government, PTS Granada, 18016 Granada, Spain.
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Li J, Jia K, Zhao W, Yuan B, Liu Y. Natural and socio-environmental factors contribute to the transmissibility of COVID-19: evidence from an improved SEIR model. INTERNATIONAL JOURNAL OF BIOMETEOROLOGY 2023; 67:1789-1802. [PMID: 37561207 DOI: 10.1007/s00484-023-02539-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 06/28/2023] [Accepted: 08/01/2023] [Indexed: 08/11/2023]
Abstract
COVID-19 has ravaged Brazil, and its spread showed spatial heterogeneity. Changes in the environment have been implicated as potential factors involved in COVID-19 transmission. However, considerable research efforts have not elucidated the risk of environmental factors on COVID-19 transmission from the perspective of infectious disease dynamics. The aim of this study is to model the influence of the environment on COVID-19 transmission and to analyze how the socio-ecological factors affecting the probability of virus transmission in 10 states dramatically shifted during the early stages of the epidemic in Brazil. First, this study used a Pearson correlation to analyze the interconnection between COVID-19 morbidity and socio-ecological factors and identified factors with significant correlations as the dominant factors affecting COVID-19 transmission. Then, the time-lag effect of dominant factors on the morbidity of COVID-19 was investigated by constructing a distributed lag nonlinear model and standard two-stage meta-analytic model, and the results were considered in the improved SEIR model. Lastly, a machine learning method was introduced to explore the nonlinear relationship between the environmental propagation probability and socio-ecological factors. By analyzing the impact of environmental factors on virus transmission, it can be found that population mobility directly caused by human activities had a greater impact on virus transmission than temperature and humidity. The heterogeneity of meteorological factors can be accounted for by the diverse climate patterns in Brazil. The improved SEIR model was adopted to explore the interconnection of COVID-19 transmission and the environment, which revealed a new strategy to probe the causal links between them.
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Affiliation(s)
- Jie Li
- State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
| | - Kun Jia
- State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China.
| | - Wenwu Zhao
- Stake Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
- Institute of Land Surface System and Sustainable Development, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
| | - Bo Yuan
- State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
| | - Yanxu Liu
- Stake Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
- Institute of Land Surface System and Sustainable Development, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
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8
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Wang H, Jiang B, Zhao Q, Zhou C, Ma W. Temperature extremes and infectious diarrhea in China: attributable risks and effect modification of urban characteristics. INTERNATIONAL JOURNAL OF BIOMETEOROLOGY 2023; 67:1659-1668. [PMID: 37500794 DOI: 10.1007/s00484-023-02528-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 07/13/2023] [Accepted: 07/17/2023] [Indexed: 07/29/2023]
Abstract
Studies about the role of urban characteristics in modifying the health effect of temperature extremes are still unclear. This study is aimed at quantifying the morbidity risk of infectious diarrhea attributable to temperature extremes and the modified effect of a range of city-specific indicators. Distributed lag non-linear model and multivariate meta-regression were applied to estimate fractions of infectious diarrhea morbidity attributable to temperature extremes and to explore the effect modification of city-level characteristics. Extreme heat- and extreme cold-related infectious diarrhea amounted to 0.99% (95% CI: 0.57-1.29) and 1.05% (95% CI: 0.64-1.24) of the total cases, respectively. The attributable fraction of temperature extremes on infectious diarrhea varied between southern and northern China. Several city characteristics modified the association of extreme cold with infectious diarrhea, with a higher morbidity impact related to increased water consumption per capita and decreased latitude. Regions with higher levels of latitude or GDP per capita appeared to be more sensitive to extreme hot. In conclusion, exposure to temperature extremes was associated with increased risks of infectious diarrhea and the effect can be modified by urban characteristics. This finding can inform public health interventions to decrease the adverse effects of temperature extremes on infectious diarrhea.
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Affiliation(s)
- Haitao Wang
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong Province, China
- Shandong University Climate Change and Health Center, Jinan, Shandong Province, China
| | - Baofa Jiang
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong Province, China
- Shandong University Climate Change and Health Center, Jinan, Shandong Province, China
| | - Qi Zhao
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong Province, China
- Shandong University Climate Change and Health Center, Jinan, Shandong Province, China
| | - Chengchao Zhou
- Centre for Health Management and Policy Research, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong Province, China
- NHC Key Laboratory of Health Economics and Policy Research, Shandong University, Jinan, Shandong Province, China
| | - Wei Ma
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong Province, China.
- Shandong University Climate Change and Health Center, Jinan, Shandong Province, China.
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Jenkins GS, Freire SM, Ogunro T, Niang D, Andrade M, Drame MS, Huvi JB, Pires EES, Toure EN, Camara M. COVID-19 New Cases and Environmental Factors During Wet and Dry Seasons in West and Southern Africa. GEOHEALTH 2023; 7:e2022GH000765. [PMID: 37519911 PMCID: PMC10383768 DOI: 10.1029/2022gh000765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 05/08/2023] [Accepted: 06/20/2023] [Indexed: 08/01/2023]
Abstract
Sub-Saharan Africa has been the last continent to experience a significant number of cases in the novel Coronavirus (COVID-19). Studies suggest that air pollution is related to COVID-19 mortality; poor air quality has been linked to cardiovascular, cerebrovascular, and respiratory diseases, which are considered co-morbidities linked to COVID-19 deaths. We examine potential connections between country-wide COVID-19 cases and environmental conditions in Senegal, Cabo Verde, Nigeria, Cote D'Ivorie, and Angola. We analyze PM2.5 concentrations, temperatures from cost-effective in situ measurements, aerosol optical depth (AOD), and fire count and NO2 column values from space-borne platforms from 1 January 2020 through 31 March 2021. Our results show that the first COVID-19 wave in West Africa began during the wet season of 2020, followed by a second during the dry season of 2020. In Angola, the first wave starts during the biomass burning season but does not peak until November of 2020. Overall PM2.5 concentrations are the highest in Ibadan, Nigeria, and coincided with the second wave of COVID-19 in late 2021 and early 2022. The COVID-19 waves in Cabo Verde are not in phase with those in Senegal, Nigeria, and Cote, lagging by several months in general. Overall, the highest correlations occurred between weekly new COVID-19 cases meteorological and air quality variables occurred in the dry season.
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Affiliation(s)
- G. S. Jenkins
- Alliance for Education, Science, Engineering and Design with Africa (AESEDA)Pennsylvania State UniversityUniversity ParkPAUSA
| | | | | | - D. Niang
- Cheikh Anta Diop UniversityDakarSenegal
| | | | | | - J. B. Huvi
- Instituto Superior de Ciências da Educação de Benguela ‐ AngolaBenguelaAngola
| | - E. E. S. Pires
- Centro de Estudos e Pesquisa do TundavalaEngineering DepartmentISPTundavalaLubangoAngola
| | - E. N. Toure
- University Felix Houphouet BiognyAbidjanCote D'Ivorie
| | - M. Camara
- University of Assane SeckZiguinchorSenegal
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Zhang R, Lai KY, Liu W, Liu Y, Cai W, Webster C, Luo L, Sarkar C. Association of climatic variables with risk of transmission of influenza in Guangzhou, China, 2005-2021. Int J Hyg Environ Health 2023; 252:114217. [PMID: 37418782 DOI: 10.1016/j.ijheh.2023.114217] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 06/16/2023] [Accepted: 06/29/2023] [Indexed: 07/09/2023]
Abstract
BACKGROUND Climatic variables constitute important extrinsic determinants of transmission and seasonality of influenza. Yet quantitative evidence of independent associations of viral transmissibility with climatic factors has thus far been scarce and little is known about the potential effects of interactions between climatic factors on transmission. OBJECTIVE This study aimed to examine the associations of key climatic factors with risk of influenza transmission in subtropical Guangzhou. METHODS Influenza epidemics were identified over a 17-year period using the moving epidemic method (MEM) from a dataset of N = 295,981 clinically- and laboratory-confirmed cases of influenza in Guangzhou. Data on eight key climatic variables were collected from China Meteorological Data Service Centre. Generalized additive model combined with the distributed lag non-linear model (DLNM) were developed to estimate the exposure-lag-response curve showing the trajectory of instantaneous reproduction number (Rt) across the distribution of each climatic variable after adjusting for depletion of susceptible, inter-epidemic effect and school holidays. The potential interaction effects of temperature, humidity and rainfall on influenza transmission were also examined. RESULTS Over the study period (2005-21), 21 distinct influenza epidemics with varying peak timings and durations were identified. Increasing air temperature, sunshine, absolute and relative humidity were significantly associated with lower Rt, while the associations were opposite in the case of ambient pressure, wind speed and rainfall. Rainfall, relative humidity, and ambient temperature were the top three climatic contributors to variance in transmissibility. Interaction models found that the detrimental association between high relative humidity and transmissibility was more pronounced at high temperature and rainfall. CONCLUSION Our findings are likely to help understand the complex role of climatic factors in influenza transmission, guiding informed climate-related mitigation and adaptation policies to reduce transmission in high density subtropical cities.
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Affiliation(s)
- Rong Zhang
- Healthy High Density Cities Lab, HKUrbanLab, The University of Hong Kong, Knowles Building, Pokfulam Road, Pokfulam, Hong Kong, China; Department of Urban Planning and Design, The University of Hong Kong, Knowles Building, Pokfulam Road, Pokfulam, Hong Kong, China
| | - Ka Yan Lai
- Healthy High Density Cities Lab, HKUrbanLab, The University of Hong Kong, Knowles Building, Pokfulam Road, Pokfulam, Hong Kong, China; Department of Urban Planning and Design, The University of Hong Kong, Knowles Building, Pokfulam Road, Pokfulam, Hong Kong, China
| | - Wenhui Liu
- Guangzhou Center for Disease Control and Prevention, Guangzhou, Guangdong, China
| | - Yanhui Liu
- Guangzhou Center for Disease Control and Prevention, Guangzhou, Guangdong, China
| | - Wenfeng Cai
- Guangzhou Center for Disease Control and Prevention, Guangzhou, Guangdong, China
| | - Chris Webster
- Healthy High Density Cities Lab, HKUrbanLab, The University of Hong Kong, Knowles Building, Pokfulam Road, Pokfulam, Hong Kong, China; Department of Urban Planning and Design, The University of Hong Kong, Knowles Building, Pokfulam Road, Pokfulam, Hong Kong, China; Urban Systems Institute, The University of Hong Kong, Hong Kong, China
| | - Lei Luo
- Guangzhou Center for Disease Control and Prevention, Guangzhou, Guangdong, China.
| | - Chinmoy Sarkar
- Healthy High Density Cities Lab, HKUrbanLab, The University of Hong Kong, Knowles Building, Pokfulam Road, Pokfulam, Hong Kong, China; Department of Urban Planning and Design, The University of Hong Kong, Knowles Building, Pokfulam Road, Pokfulam, Hong Kong, China; Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, OX3 7JX, UK; Urban Systems Institute, The University of Hong Kong, Hong Kong, China.
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Vicedo-Cabrera AM, de Schrijver E, Schumacher DL, Ragettli MS, Fischer EM, Seneviratne SI. The footprint of human-induced climate change on heat-related deaths in the summer of 2022 in Switzerland. ENVIRONMENTAL RESEARCH LETTERS : ERL [WEB SITE] 2023; 18:074037. [PMID: 38476980 PMCID: PMC7615730 DOI: 10.1088/1748-9326/ace0d0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 03/14/2024]
Abstract
Human-induced climate change is leading to an increase in the intensity and frequency of extreme weather events, which are severely affecting the health of the population. The exceptional heat during the summer of 2022 in Europe is an example, with record-breaking temperatures only below the infamous 2003 summer. High ambient temperatures are associated with many health outcomes, including premature mortality. However, there is limited quantitative evidence on the contribution of anthropogenic activities to the substantial heat-related mortality observed in recent times. Here we combined methods in climate epidemiology and attribution to quantify the heat-related mortality burden attributed to human-induced climate change in Switzerland during the summer of 2022. We first estimated heat-mortality association in each canton and age/sex population between 1990 and 2017 in a two-stage time-series analysis. We then calculated the mortality attributed to heat in the summer of 2022 using observed mortality, and compared it with the hypothetical heat-related burden that would have occurred in absence of human-induced climate change. This counterfactual scenario was derived by regressing the Swiss average temperature against global mean temperature in both observations and CMIP6 models. We estimate 623 deaths [95% empirical confidence interval (95% eCI): 151-1068] due to heat between June and August 2022, corresponding to 3.5% of all-cause mortality. More importantly, we find that 60% of this burden (370 deaths [95% eCI: 133-644]) could have been avoided in absence of human-induced climate change. Older women were affected the most, as well as populations in western and southern Switzerland and more urbanized areas. Our findings demonstrate that human-induced climate change was a relevant driver of the exceptional excess health burden observed in the 2022 summer in Switzerland.
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Affiliation(s)
- Ana M Vicedo-Cabrera
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Oeschger Center for Climate Change Research, University of Bern, Bern, Switzerland
| | - Evan de Schrijver
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Oeschger Center for Climate Change Research, University of Bern, Bern, Switzerland
- Graduate School of Health Sciences, University of Bern, Bern, Switzerland
| | | | - Martina S Ragettli
- Swiss Tropical and Public Health Institute (SwissTPH), Allschwil, Switzerland
- University of Basel, Basel, Switzerland
| | - Erich M Fischer
- Institute for Atmospheric and Climate Science, ETH Zürich, Zürich, Switzerland
| | - Sonia I Seneviratne
- Institute for Atmospheric and Climate Science, ETH Zürich, Zürich, Switzerland
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12
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Badr HS, Zaitchik BF, Kerr GH, Nguyen NLH, Chen YT, Hinson P, Colston JM, Kosek MN, Dong E, Du H, Marshall M, Nixon K, Mohegh A, Goldberg DL, Anenberg SC, Gardner LM. Unified real-time environmental-epidemiological data for multiscale modeling of the COVID-19 pandemic. Sci Data 2023; 10:367. [PMID: 37286690 PMCID: PMC10245354 DOI: 10.1038/s41597-023-02276-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 05/30/2023] [Indexed: 06/09/2023] Open
Abstract
An impressive number of COVID-19 data catalogs exist. However, none are fully optimized for data science applications. Inconsistent naming and data conventions, uneven quality control, and lack of alignment between disease data and potential predictors pose barriers to robust modeling and analysis. To address this gap, we generated a unified dataset that integrates and implements quality checks of the data from numerous leading sources of COVID-19 epidemiological and environmental data. We use a globally consistent hierarchy of administrative units to facilitate analysis within and across countries. The dataset applies this unified hierarchy to align COVID-19 epidemiological data with a number of other data types relevant to understanding and predicting COVID-19 risk, including hydrometeorological data, air quality, information on COVID-19 control policies, vaccine data, and key demographic characteristics.
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Affiliation(s)
- Hamada S Badr
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
- Department of Earth and Planetary Sciences, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Benjamin F Zaitchik
- Department of Earth and Planetary Sciences, Johns Hopkins University, Baltimore, MD, 21218, USA.
| | - Gaige H Kerr
- Department of Environmental and Occupational Health, Milken Institute School of Public Health, George Washington University, Washington, DC, 20052, USA
| | - Nhat-Lan H Nguyen
- College of Arts and Sciences, University of Virginia, Charlottesville, VA, 22903, USA
| | - Yen-Ting Chen
- Division of Infectious Diseases and International Health, University of Virginia School of Medicine, Charlottesville, VA, 22903, USA
- Department of Emergency Medicine, Chi-Mei Medical Center, Tainan, Taiwan
| | - Patrick Hinson
- College of Arts and Sciences, University of Virginia, Charlottesville, VA, 22903, USA
- Division of Infectious Diseases and International Health, University of Virginia School of Medicine, Charlottesville, VA, 22903, USA
| | - Josh M Colston
- Division of Infectious Diseases and International Health, University of Virginia School of Medicine, Charlottesville, VA, 22903, USA
| | - Margaret N Kosek
- Division of Infectious Diseases and International Health, University of Virginia School of Medicine, Charlottesville, VA, 22903, USA
| | - Ensheng Dong
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Hongru Du
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Maximilian Marshall
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Kristen Nixon
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Arash Mohegh
- Department of Environmental and Occupational Health, Milken Institute School of Public Health, George Washington University, Washington, DC, 20052, USA
- Health & Exposure Assessment Branch, California Air Resources Board, Sacramento, CA, 95812, USA
| | - Daniel L Goldberg
- Department of Environmental and Occupational Health, Milken Institute School of Public Health, George Washington University, Washington, DC, 20052, USA
| | - Susan C Anenberg
- Department of Environmental and Occupational Health, Milken Institute School of Public Health, George Washington University, Washington, DC, 20052, USA
| | - Lauren M Gardner
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
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13
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Wang Y, Lyu Y, Tong S, Ding C, Wei L, Zhai M, Xu K, Hao R, Wang X, Li N, Luo Y, Li Y, Wang J. Association between meteorological factors and COVID-19 transmission in low- and middle-income countries: A time-stratified case-crossover study. ENVIRONMENTAL RESEARCH 2023; 231:116088. [PMID: 37169140 PMCID: PMC10166718 DOI: 10.1016/j.envres.2023.116088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 04/23/2023] [Accepted: 05/08/2023] [Indexed: 05/13/2023]
Abstract
BACKGROUND Evidence is limited regarding the association between meteorological factors and COVID-19 transmission in low- and middle-income countries (LMICs). OBJECTIVE To investigate the independent and interactive effects of temperature, relative humidity (RH), and ultraviolet (UV) radiation on the spread of COVID-19 in LMICs. METHODS We collected daily data on COVID-19 confirmed cases, meteorological factors and non-pharmaceutical interventions (NPIs) in 2143 city- and district-level sites from 6 LMICs during 2020. We applied a time-stratified case-crossover design with distributed lag nonlinear model to evaluate the independent and interactive effects of meteorological factors on COVID-19 transmission after controlling NPIs. We generated an overall estimate through pooling site-specific relative risks (RR) using a multivariate meta-regression model. RESULTS There was a positive, non-linear, association between temperature and COVID-19 confirmed cases in all study sites, while RH and UV showed negative non-linear associations. RR of the 90th percentile temperature (28.1 °C) was 1.14 [95% confidence interval (CI): 1.02, 1.28] compared with the 50th percentile temperature (24.4 °C). RR of the10th percentile UV was 1.41 (95% CI: 1.29, 1.54). High temperature and high RH were associated with increased risks in temperate climate but decreased risks in tropical climate, while UV exhibited a consistent, negative association across climate zones. Temperature, RH, and UV interacted to affect COVID-19 transmission. Temperature and RH also showed higher risks in low NPIs sites. CONCLUSION Temperature, RH, and UV appeared to independently and interactively affect the transmission of COVID-19 in LMICs but such associations varied with climate zones. Our results suggest that more attention should be paid to meteorological variation when the transmission of COVID-19 is still rampant in LMICs.
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Affiliation(s)
- Yu Wang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Yiran Lyu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Shilu Tong
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China; Shanghai Children's Medical Center, Shanghai Jiao Tong University, Shanghai, 200025, China; School of Public Health, Institute of Environment and Population Health, Anhui Medical University, Hefei, 230032, China; Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China; School of Public Health and Social Work, Queensland University of Technology, Brisbane, 4000, Australia
| | - Cheng Ding
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Lan Wei
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Mengying Zhai
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Kaiqiang Xu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China; School of Public Health, Hebei University, Hebei, 071000, China
| | - Ruiting Hao
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Xiaochen Wang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Na Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Yueyun Luo
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Yonghong Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Jiao Wang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China.
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14
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Chen F, Chen S, Huang H, Deng Y, Yang W. Macro-analysis of climatic factors for COVID-19 pandemic based on Köppen-Geiger climate classification. CHAOS (WOODBURY, N.Y.) 2023; 33:2887744. [PMID: 37125936 DOI: 10.1063/5.0144099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Accepted: 04/03/2023] [Indexed: 05/03/2023]
Abstract
This study integrated dynamic models and statistical methods to design a novel macroanalysis approach to judge the climate impacts. First, the incidence difference across Köppen-Geiger climate regions was used to determine the four risk areas. Then, the effective influence of climate factors was proved according to the non-climate factors' non-difference among the risk areas, multi-source non-major component data assisting the proof. It is found that cold steppe arid climates and wet temperate climates are more likely to transmit SARS-CoV-2 among human beings. Although the results verified that the global optimum temperature was around 10 °C, and the average humidity was 71%, there was evident heterogeneity among different climate risk areas. The first-grade and fourth-grade risk regions in the Northern Hemisphere and fourth-grade risk regions in the Southern Hemisphere are more sensitive to temperature. However, the third-grade risk region in the Southern Hemisphere is more sensitive to relative humidity. The Southern Hemisphere's third-grade and fourth-grade risk regions are more sensitive to precipitation.
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Affiliation(s)
- Fangyuan Chen
- School of Arts and Sciences, Beijing Institute of Fashion Technology, Beijing 100029, China
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - Siya Chen
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - Hua Huang
- Department of Radiology, The Third People's Hospital of Shenzhen, The Second Affiliated Hospital of Southern University of Science and Technology, National Clinical Research Center for Infectious Diseases, Shenzhen 518112, China
| | - Yingying Deng
- Department of Radiology, Shenzhen Yantian District People's Hospital, Shenzhen 518081, China
| | - Weizhong Yang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
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15
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Lin J, Huang B, Kwan MP, Chen M, Wang Q. COVID-19 infection rate but not severity is associated with availability of greenness in the United States. LANDSCAPE AND URBAN PLANNING 2023; 233:104704. [PMID: 36718417 PMCID: PMC9870763 DOI: 10.1016/j.landurbplan.2023.104704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/05/2022] [Revised: 01/14/2023] [Accepted: 01/20/2023] [Indexed: 06/18/2023]
Abstract
Human exposure to greenness is associated with COVID-19 prevalence and severity, but most relevant research has focused on the relationships between greenness and COVID-19 infection rates. In contrast, relatively little is known about the associations between greenness and COVID-19 hospitalizations and deaths, which are important for risk assessment, resource allocation, and intervention strategies. Moreover, it is unclear whether greenness could help reduce health inequities by offering more benefits to disadvantaged populations. Here, we estimated the associations between availability of greenness (expressed as population-density-weighted normalized difference vegetation index) and COVID-19 outcomes across the urban-rural continuum gradient in the United States using generalized additive models with a negative binomial distribution. We aggregated individual COVID-19 records at the county level, which includes 3,040 counties for COVID-19 case infection rates, 1,397 counties for case hospitalization rates, and 1,305 counties for case fatality rates. Our area-level ecological study suggests that although availability of greenness shows null relationships with COVID-19 case hospitalization and fatality rates, COVID-19 infection rate is statistically significant and negatively associated with more greenness availability. When performing stratified analyses by different sociodemographic groups, availability of greenness shows stronger negative associations for men than for women, and for adults than for the elderly. This indicates that greenness might have greater health benefits for the former than the latter, and thus has limited effects for ameliorating COVID-19 related inequity. The revealed greenness-COVID-19 links across different space, time and sociodemographic groups provide working hypotheses for the targeted design of nature-based interventions and greening policies to benefit human well-being and reduce health inequity. This has important implications for the post-pandemic recovery and future public health crises.
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Affiliation(s)
- Jian Lin
- Sierra Nevada Research Institute, University of California, Merced, Merced, CA, 95340, USA
| | - Bo Huang
- Department of Geography and Resource Management, The Chinese University of Hong Kong, Shatin, Hong Kong, China
- Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Shatin, Hong Kong, China
| | - Mei-Po Kwan
- Department of Geography and Resource Management, The Chinese University of Hong Kong, Shatin, Hong Kong, China
- Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Shatin, Hong Kong, China
- Department of Human Geography and Spatial Planning, Utrecht University, 3584 CB Utrecht, The Netherlands
| | - Min Chen
- Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), Nanjing Normal University, Nanjing 210023, China
| | - Qiang Wang
- State Key Laboratory for Subtropical Mountain Ecology of the Ministry of Science and Technology and Fujian Province, Fujian Normal University, Fuzhou 350007, China
- School of Geographical Sciences, Fujian Normal University, Fuzhou 350007, China
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16
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Alves A, da Costa NM, Morgado P, da Costa EM. Uncovering COVID-19 infection determinants in Portugal: towards an evidence-based spatial susceptibility index to support epidemiological containment policies. Int J Health Geogr 2023; 22:8. [PMID: 37024965 PMCID: PMC10078027 DOI: 10.1186/s12942-023-00329-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 03/28/2023] [Indexed: 04/08/2023] Open
Abstract
BACKGROUND COVID-19 caused the largest pandemic of the twenty-first century forcing the adoption of containment policies all over the world. Many studies on COVID-19 health determinants have been conducted, mainly using multivariate methods and geographic information systems (GIS), but few attempted to demonstrate how knowing social, economic, mobility, behavioural, and other spatial determinants and their effects can help to contain the disease. For example, in mainland Portugal, non-pharmacological interventions (NPI) were primarily dependent on epidemiological indicators and ignored the spatial variation of susceptibility to infection. METHODS We present a data-driven GIS-multicriteria analysis to derive a spatial-based susceptibility index to COVID-19 infection in Portugal. The cumulative incidence over 14 days was used in a stepwise multiple linear regression as the target variable along potential determinants at the municipal scale. To infer the existence of thresholds in the relationships between determinants and incidence the most relevant factors were examined using a bivariate Bayesian change point analysis. The susceptibility index was mapped based on these thresholds using a weighted linear combination. RESULTS Regression results support that COVID-19 spread in mainland Portugal had strong associations with factors related to socio-territorial specificities, namely sociodemographic, economic and mobility. Change point analysis revealed evidence of nonlinearity, and the susceptibility classes reflect spatial dependency. The spatial index of susceptibility to infection explains with accuracy previous and posterior infections. Assessing the NPI levels in relation to the susceptibility map points towards a disagreement between the severity of restrictions and the actual propensity for transmission, highlighting the need for more tailored interventions. CONCLUSIONS This article argues that NPI to contain COVID-19 spread should consider the spatial variation of the susceptibility to infection. The findings highlight the importance of customising interventions to specific geographical contexts due to the uneven distribution of COVID-19 infection determinants. The methodology has the potential for replication at other geographical scales and regions to better understand the role of health determinants in explaining spatiotemporal patterns of diseases and promoting evidence-based public health policies.
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Affiliation(s)
- André Alves
- Centre of Geographical Studies, Institute of Geography and Spatial Planning, University of Lisbon, 1600-276, Lisbon, Portugal.
| | - Nuno Marques da Costa
- Centre of Geographical Studies, Institute of Geography and Spatial Planning, University of Lisbon, 1600-276, Lisbon, Portugal
- Associate Laboratory TERRA, 1349-017, Lisbon, Portugal
| | - Paulo Morgado
- Centre of Geographical Studies, Institute of Geography and Spatial Planning, University of Lisbon, 1600-276, Lisbon, Portugal
- Associate Laboratory TERRA, 1349-017, Lisbon, Portugal
| | - Eduarda Marques da Costa
- Centre of Geographical Studies, Institute of Geography and Spatial Planning, University of Lisbon, 1600-276, Lisbon, Portugal
- Associate Laboratory TERRA, 1349-017, Lisbon, Portugal
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17
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Paireau J, Charpignon ML, Larrieu S, Calba C, Hozé N, Boëlle PY, Thiebaut R, Prague M, Cauchemez S. Impact of non-pharmaceutical interventions, weather, vaccination, and variants on COVID-19 transmission across departments in France. BMC Infect Dis 2023; 23:190. [PMID: 36997873 PMCID: PMC10061408 DOI: 10.1186/s12879-023-08106-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 02/20/2023] [Indexed: 04/01/2023] Open
Abstract
BACKGROUND Multiple factors shape the temporal dynamics of the COVID-19 pandemic. Quantifying their relative contributions is key to guide future control strategies. Our objective was to disentangle the individual effects of non-pharmaceutical interventions (NPIs), weather, vaccination, and variants of concern (VOC) on local SARS-CoV-2 transmission. METHODS We developed a log-linear model for the weekly reproduction number (R) of hospital admissions in 92 French metropolitan departments. We leveraged (i) the homogeneity in data collection and NPI definitions across departments, (ii) the spatial heterogeneity in the timing of NPIs, and (iii) an extensive observation period (14 months) covering different weather conditions, VOC proportions, and vaccine coverage levels. FINDINGS Three lockdowns reduced R by 72.7% (95% CI 71.3-74.1), 70.4% (69.2-71.6) and 60.7% (56.4-64.5), respectively. Curfews implemented at 6/7 pm and 8/9 pm reduced R by 34.3% (27.9-40.2) and 18.9% (12.04-25.3), respectively. School closures reduced R by only 4.9% (2.0-7.8). We estimated that vaccination of the entire population would have reduced R by 71.7% (56.4-81.6), whereas the emergence of VOC (mainly Alpha during the study period) increased transmission by 44.6% (36.1-53.6) compared with the historical variant. Winter weather conditions (lower temperature and absolute humidity) increased R by 42.2% (37.3-47.3) compared to summer weather conditions. Additionally, we explored counterfactual scenarios (absence of VOC or vaccination) to assess their impact on hospital admissions. INTERPRETATION Our study demonstrates the strong effectiveness of NPIs and vaccination and quantifies the role of weather while adjusting for other confounders. It highlights the importance of retrospective evaluation of interventions to inform future decision-making.
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Affiliation(s)
- Juliette Paireau
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Université Paris Cité, CNRS UMR 2000, Paris, France.
- Infectious Diseases Department, Santé Publique France, Saint Maurice, France.
| | - Marie-Laure Charpignon
- Institute for Data, Systems, and Society (IDSS), Cambridge, MA, USA
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA
- University of Bordeaux, Inria Bordeaux Sud-Ouest, Inserm, Bordeaux Population Health Research Center, SISTM Team, UMR1219, Bordeaux, France
| | - Sophie Larrieu
- Regions Department, Regional Office Nouvelle-Aquitaine, Santé publique France, Bordeaux, France
| | - Clémentine Calba
- Regions Department, Regional Office Provence-Alps-French Riviera and Corsica, Santé Publique France, Marseille, France
| | - Nathanaël Hozé
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Université Paris Cité, CNRS UMR 2000, Paris, France
| | - Pierre-Yves Boëlle
- INSERM, Sorbonne Université, Institut Pierre Louis d'Epidémiologie et de Santé Publique, Paris, France
| | - Rodolphe Thiebaut
- University of Bordeaux, Inria Bordeaux Sud-Ouest, Inserm, Bordeaux Population Health Research Center, SISTM Team, UMR1219, Bordeaux, France
| | - Mélanie Prague
- University of Bordeaux, Inria Bordeaux Sud-Ouest, Inserm, Bordeaux Population Health Research Center, SISTM Team, UMR1219, Bordeaux, France
| | - Simon Cauchemez
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Université Paris Cité, CNRS UMR 2000, Paris, France
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18
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Kerr GH, Badr HS, Barbieri AF, Colston JM, Gardner LM, Kosek MN, Zaitchik BF. Evolving Drivers of Brazilian SARS-CoV-2 Transmission: A Spatiotemporally Disaggregated Time Series Analysis of Meteorology, Policy, and Human Mobility. GEOHEALTH 2023; 7:e2022GH000727. [PMID: 36960326 PMCID: PMC10030230 DOI: 10.1029/2022gh000727] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 02/17/2023] [Accepted: 03/06/2023] [Indexed: 06/09/2023]
Abstract
Brazil has been severely affected by the COVID-19 pandemic. Temperature and humidity have been purported as drivers of SARS-CoV-2 transmission, but no consensus has been reached in the literature regarding the relative roles of meteorology, governmental policy, and mobility on transmission in Brazil. We compiled data on meteorology, governmental policy, and mobility in Brazil's 26 states and one federal district from June 2020 to August 2021. Associations between these variables and the time-varying reproductive number (R t ) of SARS-CoV-2 were examined using generalized additive models fit to data from the entire 15-month period and several shorter, 3-month periods. Accumulated local effects and variable importance metrics were calculated to analyze the relationship between input variables and R t . We found that transmission is strongly influenced by unmeasured sources of between-state heterogeneity and the near-recent trajectory of the pandemic. Increased temperature generally was associated with decreased transmission and increased specific humidity with increased transmission. However, the impacts of meteorology, policy, and mobility on R t varied in direction, magnitude, and significance across our study period. This time variance could explain inconsistencies in the published literature to date. While meteorology weakly modulates SARS-CoV-2 transmission, daily or seasonal weather variations alone will not stave off future surges in COVID-19 cases in Brazil. Investigating how the roles of environmental factors and disease control interventions may vary with time should be a deliberate consideration of future research on the drivers of SARS-CoV-2 transmission.
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Affiliation(s)
- Gaige Hunter Kerr
- Department of Environmental and Occupational HealthGeorge Washington UniversityWashingtonDCUSA
| | - Hamada S. Badr
- Department of Civil and Systems EngineeringJohns Hopkins UniversityBaltimoreMDUSA
- Now at Sales, Market, and Global ServicesAmazon Web ServicesSeattleWAUSA
| | - Alisson F. Barbieri
- Demography DepartmentUniversidade Federal de Minas GeraisBelo HorizonteBrazil
| | - Josh M. Colston
- Division of Infectious Diseases and International HealthUniversity of Virginia School of MedicineCharlottesvilleVAUSA
| | - Lauren M. Gardner
- Department of Civil and Systems EngineeringJohns Hopkins UniversityBaltimoreMDUSA
| | - Margaret N. Kosek
- Division of Infectious Diseases and International HealthUniversity of Virginia School of MedicineCharlottesvilleVAUSA
| | - Benjamin F. Zaitchik
- Department of Earth and Planetary SciencesJohns Hopkins UniversityBaltimoreMDUSA
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19
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Colston JM, Hinson P, Nguyen NLH, Chen YT, Badr HS, Kerr GH, Gardner LM, Martin DN, Quispe AM, Schiaffino F, Kosek MN, Zaitchik BF. Effects of hydrometeorological and other factors on SARS-CoV-2 reproduction number in three contiguous countries of tropical Andean South America: a spatiotemporally disaggregated time series analysis. IJID REGIONS 2023; 6:29-41. [PMID: 36437857 PMCID: PMC9675637 DOI: 10.1016/j.ijregi.2022.11.007] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 11/14/2022] [Accepted: 11/15/2022] [Indexed: 06/09/2023]
Abstract
Background The COVID-19 pandemic has caused societal disruption globally, and South America has been hit harder than other lower-income regions. This study modeled the effects of six weather variables on district-level SARS-CoV-2 reproduction numbers (Rt ) in three contiguous countries of tropical Andean South America (Colombia, Ecuador, and Peru), adjusting for environmental, policy, healthcare infrastructural and other factors. Methods Daily time-series data on SARS-CoV-2 infections were sourced from the health authorities of the three countries at the smallest available administrative level. Rt values were calculated and merged by date and unit ID with variables from a unified COVID-19 dataset and other publicly available sources for May-December, 2020. Generalized additive models were fitted. Findings Relative humidity and solar radiation were inversely associated with SARS-CoV-2 Rt . Days with radiation above 1000 kJ/m2 saw a 1.3% reduction in Rt , and those with humidity above 50% recorded a 0.9% reduction in Rt . Transmission was highest in densely populated districts, and lowest in districts with poor healthcare access and on days with lowest population mobility. Wind speed, temperature, region, aggregate government policy response, and population age structure had little impact. The fully adjusted model explained 4.3% of Rt variance. Interpretation Dry atmospheric conditions of low humidity increase district-level SARS-CoV-2 reproduction numbers, while higher levels of solar radiation decrease district-level SARS-CoV-2 reproduction numbers - effects that are comparable in magnitude to population factors like lockdown compliance. Weather monitoring could be incorporated into disease surveillance and early warning systems in conjunction with more established risk indicators and surveillance measures. Funding NASA's Group on Earth Observations Work Programme (16-GEO16-0047).
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Affiliation(s)
- Josh M. Colston
- Division of Infectious Diseases and International Health, University of Virginia School of Medicine, Charlottesville, VA, 22903, USA
| | - Patrick Hinson
- College of Arts and Sciences, University of Virginia, VA, USA
| | | | - Yen Ting Chen
- Department of Emergency Medicine, Chi-Mei Medical Center, Tainan, Taiwan
| | - Hamada S. Badr
- Department of Earth and Planetary Sciences, Johns Hopkins Krieger School of Arts and Sciences, Baltimore, MD, 21218, USA
| | - Gaige H. Kerr
- Department of Environmental and Occupational Health, Milken Institute School of Public Health, George Washington University, Washington, DC, USA
| | - Lauren M. Gardner
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - David N. Martin
- Claude Moore Health Sciences Library, University of Virginia School of Medicine, VA, USA
| | | | - Francesca Schiaffino
- Faculty of Veterinary Medicine, Universidad Peruana Cayetano Heredia, Lima, Peru
- Division of Infectious Diseases and International Health and Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22903, USA
| | - Margaret N. Kosek
- Division of Infectious Diseases and International Health and Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22903, USA
| | - Benjamin F. Zaitchik
- Department of Environmental and Occupational Health, Milken Institute School of Public Health, George Washington University, Washington, DC, USA
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20
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McClymont H, Si X, Hu W. Using weather factors and google data to predict COVID-19 transmission in Melbourne, Australia: A time-series predictive model. Heliyon 2023; 9:e13782. [PMID: 36845036 PMCID: PMC9941072 DOI: 10.1016/j.heliyon.2023.e13782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 02/09/2023] [Accepted: 02/10/2023] [Indexed: 02/23/2023] Open
Abstract
Background Forecast models have been essential in understanding COVID-19 transmission and guiding public health responses throughout the pandemic. This study aims to assess the effect of weather variability and Google data on COVID-19 transmission and develop multivariable time series AutoRegressive Integrated Moving Average (ARIMA) models for improving traditional predictive modelling for informing public health policy. Methods COVID-19 case notifications, meteorological factors and Google data were collected over the B.1.617.2 (Delta) outbreak in Melbourne, Australia from August to November 2021. Timeseries cross-correlation (TSCC) was used to evaluate the temporal correlation between weather factors, Google search trends, Google Mobility data and COVID-19 transmission. Multivariable time series ARIMA models were fitted to forecast COVID-19 incidence and Effective Reproductive Number (R eff ) in the Greater Melbourne region. Five models were fitted to compare and validate predictive models using moving three-day ahead forecasts to test the predictive accuracy for both COVID-19 incidence and R eff over the Melbourne Delta outbreak. Results Case-only ARIMA model resulted in an R squared (R2) value of 0.942, Root Mean Square Error (RMSE) of 141.59, and Mean Absolute Percentage Error (MAPE) of 23.19. The model including transit station mobility (TSM) and maximum temperature (Tmax) had greater predictive accuracy with R2 0.948, RMSE 137.57, and MAPE 21.26. Conclusion Multivariable ARIMA modelling for COVID-19 cases and R eff was useful for predicting epidemic growth, with higher predictive accuracy for models including TSM and Tmax. These results suggest that TSM and Tmax would be useful for further exploration for developing weather-informed early warning models for future COVID-19 outbreaks with potential application for the inclusion of weather and Google data with disease surveillance in developing effective early warning systems for informing public health policy and epidemic response.
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21
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Chu B, Chen R, Liu Q, Wang H. Effects of High Temperature on COVID-19 Deaths in U.S. Counties. GEOHEALTH 2023; 7:e2022GH000705. [PMID: 36852181 PMCID: PMC9958002 DOI: 10.1029/2022gh000705] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 01/18/2023] [Accepted: 02/03/2023] [Indexed: 06/18/2023]
Abstract
The United States of America (USA) was afflicted by extreme heat in the summer of 2021 and some states experienced a record-hot or top-10 hottest summer. Meanwhile, the United States was also one of the countries impacted most by the coronavirus disease 2019 (COVID-19) pandemic. Growing numbers of studies have revealed that meteorological factors such as temperature may influence the number of confirmed COVID-19 cases and deaths. However, the associations between temperature and COVID-19 severity differ in various study areas and periods, especially in periods of high temperatures. Here we choose 119 US counties with large counts of COVID-19 deaths during the summer of 2021 to examine the relationship between COVID-19 deaths and temperature by applying a two-stage epidemiological analytical approach. We also calculate the years of life lost (YLL) owing to COVID-19 and the corresponding values attributable to high temperature exposure. The daily mean temperature is approximately positively correlated with COVID-19 deaths nationwide, with a relative risk of 1.108 (95% confidence interval: 1.046, 1.173) in the 90th percentile of the mean temperature distribution compared with the median temperature. In addition, 0.02 YLL per COVID-19 death attributable to high temperature are estimated at the national level, and distinct spatial variability from -0.10 to 0.08 years is observed in different states. Our results provide new evidence on the relationship between high temperature and COVID-19 deaths, which might help us to understand the underlying modulation of the COVID-19 pandemic by meteorological variables and to develop epidemic policy response strategies.
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Affiliation(s)
- Bowen Chu
- Joint International Research Laboratory of Atmospheric and Earth System SciencesSchool of Atmospheric SciencesNanjing UniversityNanjingChina
| | - Renjie Chen
- School of Public HealthKey Lab of Public Health Safety of the Ministry of Education and National Health Commission Key Lab of Health Technology AssessmentFudan UniversityShanghaiChina
| | - Qi Liu
- Joint International Research Laboratory of Atmospheric and Earth System SciencesSchool of Atmospheric SciencesNanjing UniversityNanjingChina
- Collaborative Innovation Center of Climate ChangeNanjingChina
| | - Haikun Wang
- Joint International Research Laboratory of Atmospheric and Earth System SciencesSchool of Atmospheric SciencesNanjing UniversityNanjingChina
- Collaborative Innovation Center of Climate ChangeNanjingChina
- Frontiers Science Center for Critical Earth Material CyclingNanjing UniversityNanjingChina
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22
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Du H, Dong E, Badr HS, Petrone ME, Grubaugh ND, Gardner LM. Incorporating variant frequencies data into short-term forecasting for COVID-19 cases and deaths in the USA: a deep learning approach. EBioMedicine 2023; 89:104482. [PMID: 36821889 PMCID: PMC9943054 DOI: 10.1016/j.ebiom.2023.104482] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 01/30/2023] [Accepted: 02/02/2023] [Indexed: 02/24/2023] Open
Abstract
BACKGROUND Since the US reported its first COVID-19 case on January 21, 2020, the science community has been applying various techniques to forecast incident cases and deaths. To date, providing an accurate and robust forecast at a high spatial resolution has proved challenging, even in the short term. METHOD Here we present a novel multi-stage deep learning model to forecast the number of COVID-19 cases and deaths for each US state at a weekly level for a forecast horizon of 1-4 weeks. The model is heavily data driven, and relies on epidemiological, mobility, survey, climate, demographic, and SARS-CoV-2 variant frequencies data. We implement a rigorous and robust evaluation of our model-specifically we report on weekly performance over a one-year period based on multiple error metrics, and explicitly assess how our model performance varies over space, chronological time, and different outbreak phases. FINDINGS The proposed model is shown to consistently outperform the CDC ensemble model for all evaluation metrics in multiple spatiotemporal settings, especially for the longer-term (3 and 4 weeks ahead) forecast horizon. Our case study also highlights the potential value of variant frequencies data for use in short-term forecasting to identify forthcoming surges driven by new variants. INTERPRETATION Based on our findings, the proposed forecasting framework improves upon the available state-of-the-art forecasting tools currently used to support public health decision making with respect to COVID-19 risk. FUNDING This work was funded the NSF Rapid Response Research (RAPID) grant Award ID 2108526 and the CDC Contract #75D30120C09570.
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Affiliation(s)
- Hongru Du
- Center for Systems Science and Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA; Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Ensheng Dong
- Center for Systems Science and Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA; Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Hamada S Badr
- Center for Systems Science and Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA; Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA; Department of Earth and Planetary Sciences, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Mary E Petrone
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, 06510, USA
| | - Nathan D Grubaugh
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, 06510, USA; Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, 06510, USA
| | - Lauren M Gardner
- Center for Systems Science and Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA; Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA.
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23
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De Cos O, Castillo V, Cantarero D. The Role of Functional Urban Areas in the Spread of COVID-19 Omicron (Northern Spain). J Urban Health 2023; 100:314-326. [PMID: 36829090 PMCID: PMC9955519 DOI: 10.1007/s11524-023-00720-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/25/2023] [Indexed: 02/26/2023]
Abstract
This study focuses on the space-time patterns of the COVID-19 Omicron wave at a regional scale, using municipal data. We analyze the Basque Country and Cantabria, two adjacent regions in the north of Spain, which between them numbered 491,816 confirmed cases in their 358 municipalities from 15th November 2021 to 31st March 2022. The study seeks to determine the role of functional urban areas (FUAs) in the spread of the Omicron variant of the virus, using ESRI Technology (ArcGIS Pro) and applying intelligence location methods such as 3D-bins and emerging hot spots. Those methods help identify trends and types of problem area, such as hot spots, at municipal level. The results demonstrate that FUAs do not contain an over-concentration of COVID-19 cases, as their location coefficient is under 1.0 in relation to population. Nevertheless, FUAs do have an important role as drivers of spread in the upward curve of the Omicron wave. Significant hot spot patterns are found in 85.0% of FUA area, where 98.9% of FUA cases occur. The distribution of cases shows a spatially stationary linear correlation linked to demographically progressive areas (densely populated, young profile, and with more children per woman) which are well connected by highways and railroads. Based on this research, the proposed GIS methodology can be adapted to other case studies. Considering geo-prevention and WHO Health in All Policies approaches, the research findings reveal spatial patterns that can help policymakers in tackling the pandemic in future waves as society learns to live with the virus.
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Affiliation(s)
- Olga De Cos
- Department of Geography, Urban and Regional Planning, Universidad de Cantabria, 39005 Santander, Spain
- Research Group on Health Economics and Health Services Management – Valdecilla Biomedical Research Institute (IDIVAL), 39011 Santander, Spain
| | - Valentín Castillo
- Department of Geography, Urban and Regional Planning, Universidad de Cantabria, 39005 Santander, Spain
- Research Group on Health Economics and Health Services Management – Valdecilla Biomedical Research Institute (IDIVAL), 39011 Santander, Spain
| | - David Cantarero
- Research Group on Health Economics and Health Services Management – Valdecilla Biomedical Research Institute (IDIVAL), 39011 Santander, Spain
- Department of Economics, Universidad de Cantabria, 39005 Santander, Spain
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24
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Chen F, Chen S, Jia M, Jiang M, Leng Z, Ma L, Sun Y, Zhang T, Feng L, Yang W. Exploring meteorological impacts based on Köppen-Geiger climate classification after reviewing China's response to COVID-19. APPLIED MATHEMATICAL MODELLING 2023; 114:133-146. [PMID: 36212726 PMCID: PMC9528067 DOI: 10.1016/j.apm.2022.09.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 07/24/2022] [Accepted: 09/02/2022] [Indexed: 05/17/2023]
Abstract
More than 30 months into the novel coronavirus 2019 (COVID-19) pandemic, efforts to bring this prevalence under control have achieved tentative achievements in China. However, the continuing increase in confirmed cases worldwide and the novel variants imply a severe risk of imported viruses. High-intensity non-pharmaceutical interventions (NPIs) are the mainly used measures of China's early response to COVID-19, which enabled effective control in the first wave of the epidemic. However, their efficiency is relatively low across China at the current stage. Therefore, this study focuses on whether measurable meteorological variables be found through global data to learn more about COVID-19 and explore flexible controls. This study first examines the control measures, such as NPIs and vaccination, on COVID-19 transmission across 189 countries, especially in China. Subsequently, we estimate the association between meteorological factors and time-varying reproduction numbers based on the global data by meta-population epidemic model, eliminating the aforementioned anthropogenic factors. According to this study, we find that the basic reproduction number of COVID-19 transmission varied wildly among Köppen-Geiger climate classifications, which is of great significance for the flexible adjustment of China's control protocols. We obtain that in southeast China, Köppen-Geiger climate sub-classifications, Cwb, Cfa, and Cfb, are more likely to spread COVID-19. In August, the RSIM of Cwb climate subclassification is about three times that of Dwc in April, which implies that the intensity of control efforts in different sub-regions may differ three times under the same imported risk. However, BSk and BWk, the most widely distributed in northwest China, have smaller basic reproduction numbers than Cfa, distributed in southeast coastal areas. It indicates that northwest China's control intensity could be appropriately weaker than southeast China under the same prevention objectives.
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Affiliation(s)
- Fangyuan Chen
- School of Arts and Sciences, Beijing Institute of Fashion Technology, Beijing, China
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Siya Chen
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Mengmeng Jia
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Mingyue Jiang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Zhiwei Leng
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Libing Ma
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Yanxia Sun
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Ting Zhang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Luzhao Feng
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Weizhong Yang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
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25
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Zhai G, Qi J, Zhou W, Wang J. The non-linear and interactive effects of meteorological factors on the transmission of COVID-19: A panel smooth transition regression model for cities across the globe. INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION : IJDRR 2023; 84:103478. [PMID: 36505181 PMCID: PMC9721135 DOI: 10.1016/j.ijdrr.2022.103478] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 10/14/2022] [Accepted: 12/01/2022] [Indexed: 05/11/2023]
Abstract
The ongoing pandemic created by COVID-19 has co-existed with humans for some time now, thus resulting in unprecedented disease burden. Previous studies have demonstrated the non-linear and single effects of meteorological factors on viral transmission and have a question of how to exclude the influence of unrelated confounding factors on the relationship. However, the interactions involved in such relationships remain unclear under complex weather conditions. Here, we used a panel smooth transition regression (PSTR) model to investigate the non-linear interactive impact of meteorological factors on daily new cases of COVID-19 based on a panel dataset of 58 global cities observed between Jul 1, 2020 and Jan 13, 2022. This new approach offers a possibility of assessing interactive effects of meteorological factors on daily new cases and uses fixed effects to control other unrelated confounding factors in a panel of cities. Our findings revealed that an optimal temperature range (0°C-20 °C) for the spread of COVID-19. The effect of RH (relative humidity) and DTR (diurnal temperature range) on infection became less positive (coefficient: 0.0427 to -0.0142; p < 0.05) and negative (coefficient: -0.0496 to -0.0248; p < 0.05) with increasing average temperature(T). The highest risk of infection occurred when the temperature was -10 °C and RH was >80% or when the temperature was 10 °C and DTR was 1 °C. Our findings highlight useful implications for policymakers and the general public.
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Affiliation(s)
- Guangyu Zhai
- School of Economics and Management, Lanzhou University of Technology, Lanzhou, 730050, China
| | - Jintao Qi
- School of Economics and Management, Lanzhou University of Technology, Lanzhou, 730050, China
| | - Wenjuan Zhou
- Gansu Provincial Hospital, Lanzhou, 730000, China
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26
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Nottmeyer L, Armstrong B, Lowe R, Abbott S, Meakin S, O'Reilly KM, von Borries R, Schneider R, Royé D, Hashizume M, Pascal M, Tobias A, Vicedo-Cabrera AM, Lavigne E, Correa PM, Ortega NV, Kynčl J, Urban A, Orru H, Ryti N, Jaakkola J, Dallavalle M, Schneider A, Honda Y, Ng CFS, Alahmad B, Carrasco-Escobar G, Holobâc IH, Kim H, Lee W, Íñiguez C, Bell ML, Zanobetti A, Schwartz J, Scovronick N, Coélho MDSZS, Saldiva PHN, Diaz MH, Gasparrini A, Sera F. The association of COVID-19 incidence with temperature, humidity, and UV radiation - A global multi-city analysis. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 854:158636. [PMID: 36087670 PMCID: PMC9450475 DOI: 10.1016/j.scitotenv.2022.158636] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 09/05/2022] [Accepted: 09/05/2022] [Indexed: 05/05/2023]
Abstract
BACKGROUND AND AIM The associations between COVID-19 transmission and meteorological factors are scientifically debated. Several studies have been conducted worldwide, with inconsistent findings. However, often these studies had methodological issues, e.g., did not exclude important confounding factors, or had limited geographic or temporal resolution. Our aim was to quantify associations between temporal variations in COVID-19 incidence and meteorological variables globally. METHODS We analysed data from 455 cities across 20 countries from 3 February to 31 October 2020. We used a time-series analysis that assumes a quasi-Poisson distribution of the cases and incorporates distributed lag non-linear modelling for the exposure associations at the city-level while considering effects of autocorrelation, long-term trends, and day of the week. The confounding by governmental measures was accounted for by incorporating the Oxford Governmental Stringency Index. The effects of daily mean air temperature, relative and absolute humidity, and UV radiation were estimated by applying a meta-regression of local estimates with multi-level random effects for location, country, and climatic zone. RESULTS We found that air temperature and absolute humidity influenced the spread of COVID-19 over a lag period of 15 days. Pooling the estimates globally showed that overall low temperatures (7.5 °C compared to 17.0 °C) and low absolute humidity (6.0 g/m3 compared to 11.0 g/m3) were associated with higher COVID-19 incidence (RR temp =1.33 with 95%CI: 1.08; 1.64 and RR AH =1.33 with 95%CI: 1.12; 1.57). RH revealed no significant trend and for UV some evidence of a positive association was found. These results were robust to sensitivity analysis. However, the study results also emphasise the heterogeneity of these associations in different countries. CONCLUSION Globally, our results suggest that comparatively low temperatures and low absolute humidity were associated with increased risks of COVID-19 incidence. However, this study underlines regional heterogeneity of weather-related effects on COVID-19 transmission.
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Affiliation(s)
- Luise Nottmeyer
- Faculty of Engineering Sciences, Heidelberg University, Heidelberg, Germany.
| | - Ben Armstrong
- Department of Public Health, Environments and Society, London School of Hygiene & Tropical Medicine, London, UK
| | - Rachel Lowe
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK; Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London, UK; Barcelona Supercomputing Center (BSC), Barcelona, Spain; Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain
| | - Sam Abbott
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK; Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Sophie Meakin
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK; Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Kathleen M O'Reilly
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK; Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | | | - Rochelle Schneider
- Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London, UK; Φ-Lab, European Space Agency, Frascati, Italy; European Centre for Medium-Range Weather Forecast (ECMWF), Reading, UK
| | - Dominic Royé
- Department of Geography, University of Santiago de Compostela, CIBER of Epidemiology and Public Health (CIBERESP), Spain
| | - Masahiro Hashizume
- Department of Paediatric Infectious Disease, Institute of Tropical Medicine, Nagasaki University, Japan; School of Tropical Medicine and Global Health, Nagasaki University, Japan; Department of Global Health Policy, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Mathilde Pascal
- Santé Publique France, Department of Environmental and Occupational Health, French National Public Health Agency, Saint Maurice, France
| | - Aurelio Tobias
- School of Tropical Medicine and Global Health, Nagasaki University, Japan; Institute of Environmental Assessment and Water Research (IDAEA), Spanish Council for Scientific Research (CSIC), Barcelona, Spain
| | - Ana Maria Vicedo-Cabrera
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland; Oeschger Center for Climate Change Research, University of Bern, Bern, Switzerland
| | - Eric Lavigne
- School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, Ottawa, Canada; Air Health Science Division, Health Canada, Ottawa, Canada
| | | | | | - Jan Kynčl
- Department of Infectious Diseases Epidemiology, National Institute of Public Health, Prague, Czech Republic; Department of Epidemiology and Biostatistics, Third Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Aleš Urban
- Institute of Atmospheric Physics of the Czech Academy of Sciences, Prague, Czech Republic; Faculty of Environmental Sciences, Czech University of Life Sciences, Prague, Czech Republic
| | - Hans Orru
- Department of Family Medicine and Public Health, University of Tartu, Tartu, Estonia
| | - Niilo Ryti
- Center for Environmental and Respiratory Health Research (CERH), University of Oulu, Oulu, Finland; Medical Research Center Oulu (MRC Oulu), Oulu University Hospital and University of Oulu, Oulu, Finland
| | - Jouni Jaakkola
- Center for Environmental and Respiratory Health Research (CERH), University of Oulu, Oulu, Finland; Medical Research Center Oulu (MRC Oulu), Oulu University Hospital and University of Oulu, Oulu, Finland
| | - Marco Dallavalle
- Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health (GmbH), Neuherberg, Germany; Department of Epidemiology, Institute for Medical Information Processing, Biometry and Epidemiology, Medical Faculty, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Alexandra Schneider
- Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health (GmbH), Neuherberg, Germany
| | - Yasushi Honda
- School of Tropical Medicine and Global Health, Nagasaki University, Japan; Center for Climate Change Adaptation, National Institute for Environmental Studies, Tsukuba, Japan; Faculty of Health and Sport Sciences, University of Tsukuba, Tsukuba, Japan
| | - Chris Fook Sheng Ng
- School of Tropical Medicine and Global Health, Nagasaki University, Japan; Department of Global Health Policy, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Barrak Alahmad
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Harvard University, Boston, USA
| | - Gabriel Carrasco-Escobar
- Health Innovation Laboratory, Institute of Tropical Medicine "Alexander von Humboldt", Universidad Peruana Cayetano Heredia, Lima, Peru
| | | | - Ho Kim
- Department of Public Health Science, Graduate School of Public Health & Institute of Health and Environment, Seoul National University, Seoul, Republic of Korea
| | - Whanhee Lee
- School of Biomedical Convergence Engineering, College of Information and Biomedical Engineering, Pusan National University, Yangsan, South Korea
| | - Carmen Íñiguez
- Department of Statistics and Computational Research, Universitat de València, València, Spain
| | - Michelle L Bell
- School of the Environment, Yale University, New Haven, CT, USA
| | - Antonella Zanobetti
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Harvard University, Boston, USA
| | - Joel Schwartz
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Harvard University, Boston, USA
| | - Noah Scovronick
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, USA
| | | | | | - Magali Hurtado Diaz
- Department of Environmental Health, National Institute of Public Health, Cuernavaca, Morelos, Mexico
| | - Antonio Gasparrini
- Department of Public Health, Environments and Society, London School of Hygiene & Tropical Medicine, London, UK; Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London, UK; Centre for Statistical Methodology, London School of Hygiene & Tropical Medicine, London, UK
| | - Francesco Sera
- Department of Statistics, Computer Science and Applications "G. Parenti", University of Florence, Florence, Italy.
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27
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Fuente D, Hervás D, Rebollo M, Conejero JA, Oliver N. COVID-19 outbreaks analysis in the Valencian Region of Spain in the prelude of the third wave. Front Public Health 2022; 10:1010124. [PMID: 36466513 PMCID: PMC9713945 DOI: 10.3389/fpubh.2022.1010124] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 11/02/2022] [Indexed: 11/19/2022] Open
Abstract
Introduction The COVID-19 pandemic has led to unprecedented social and mobility restrictions on a global scale. Since its start in the spring of 2020, numerous scientific papers have been published on the characteristics of the virus, and the healthcare, economic and social consequences of the pandemic. However, in-depth analyses of the evolution of single coronavirus outbreaks have been rarely reported. Methods In this paper, we analyze the main properties of all the tracked COVID-19 outbreaks in the Valencian Region between September and December of 2020. Our analysis includes the evaluation of the origin, dynamic evolution, duration, and spatial distribution of the outbreaks. Results We find that the duration of the outbreaks follows a power-law distribution: most outbreaks are controlled within 2 weeks of their onset, and only a few last more than 2 months. We do not identify any significant differences in the outbreak properties with respect to the geographical location across the entire region. Finally, we also determine the cluster size distribution of each infection origin through a Bayesian statistical model. Discussion We hope that our work will assist in optimizing and planning the resource assignment for future pandemic tracking efforts.
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Affiliation(s)
- David Fuente
- Instituto Universitario de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas, Universitat Politècnica de València, València, Spain
| | - David Hervás
- Departamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universitat Politècnica de València, València, Spain
| | - Miguel Rebollo
- Valencia Research Institute on Artificial Intelligence, Universitat Politècnica de València, València, Spain
| | - J. Alberto Conejero
- Instituto Universitario de Matemática Pura y Aplicada, Universitat Politècnica de València, València, Spain,*Correspondence: J. Alberto Conejero
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Yuan HY, Liang J, Hossain MP. Impacts of social distancing, rapid antigen test and vaccination on the Omicron outbreak during large temperature variations in Hong Kong: a modelling study. J Infect Public Health 2022; 15:1427-1435. [PMID: 36395667 PMCID: PMC9633629 DOI: 10.1016/j.jiph.2022.10.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 10/25/2022] [Accepted: 10/30/2022] [Indexed: 11/06/2022] Open
Abstract
Background The impacts of non-pharmaceutical interventions (NPIs) and vaccine boosters on the transmission of the largest outbreak of COVID-19 (the fifth wave) in Hong Kong have not been reported. The outbreak, dominated by the Omicron BA.2 subvariant, began to spread substantially after the Spring Festival in February, 2022, when the temperature varied greatly (e.g. a cold surge event). Tightening social distancing measures did not succeed in containing the outbreak until later with the use of rapid antigen tests (RAT) and increased vaccination rates. Temperature has been previously found to have significant impact on the transmissibility. Understanding how the public health interventions influence the number of infections in this outbreak provide important insights on prevention and control of COVID-19 during different seasons. Methods We developed a transmission model incorporating stratified immunity with vaccine-induced antibody responses and the daily changes in population mobility, vaccination and weather factors (i.e. temperature and relative humidity). We fitted the model to the daily reported cases detected by either PCR or RAT between 1 February and 31 March using Bayesian statistics, and quantified the effects of individual NPIs, vaccination and weather factors on transmission dynamics. Results Model predicted that, with the vaccine uptake, social distancing reduced the cumulative incidence (CI) from 58.2% to 44.5% on average. The use of RAT further reduced the CI to 39.0%. Without vaccine boosters in these two months, the CI increased to 49.1%. While public health interventions are important in reducing the total infections, the outbreak was temporarily driven by the cold surge. If the coldest two days (8.5 °C and 8.8 °C) in February were replaced by the average temperature in that month (15.2 °C), the CI would reduce from 39.0% to 28.2%. Conclusion Preventing and preparing for the transmission of COVID-19 considering the change in temperature appears to be a cost-effective preventive strategy to lead people to return to normal life.
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Affiliation(s)
- Hsiang-Yu Yuan
- Department of Biomedical Sciences, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Hong Kong SAR, China,Centre for Applied One Health Research and Policy Advice, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Hong Kong SAR, China,Corresponding author at: Department of Biomedical Sciences, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Hong Kong SAR, China
| | - Jingbo Liang
- Department of Biomedical Sciences, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Hong Kong SAR, China
| | - Md Pear Hossain
- Department of Biomedical Sciences, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Hong Kong SAR, China
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Haga L, Ruuhela R, Auranen K, Lakkala K, Heikkilä A, Gregow H. Impact of Selected Meteorological Factors on COVID-19 Incidence in Southern Finland during 2020-2021. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:13398. [PMID: 36293991 PMCID: PMC9603127 DOI: 10.3390/ijerph192013398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 10/06/2022] [Accepted: 10/09/2022] [Indexed: 06/16/2023]
Abstract
We modelled the impact of selected meteorological factors on the daily number of new cases of the coronavirus disease 2019 (COVID-19) at the Hospital District of Helsinki and Uusimaa in southern Finland from August 2020 until May 2021. We applied a DLNM (distributed lag non-linear model) with and without various environmental and non-environmental confounding factors. The relationship between the daily mean temperature or absolute humidity and COVID-19 morbidity shows a non-linear dependency, with increased incidence of COVID-19 at low temperatures between 0 to -10 °C or at low absolute humidity (AH) values below 6 g/m3. However, the outcomes need to be interpreted with caution, because the associations found may be valid only for the study period in 2020-2021. Longer study periods are needed to investigate whether severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has a seasonal pattern similar such as influenza and other viral respiratory infections. The influence of other non-environmental factors such as various mitigation measures are important to consider in future studies. Knowledge about associations between meteorological factors and COVID-19 can be useful information for policy makers and the education and health sector to predict and prepare for epidemic waves in the coming winters.
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Affiliation(s)
- Lisa Haga
- Finnish Meteorological Institute, Meteorological and Marine Research Programme, Weather and Climate Change Impact Research, P.O. Box 503, 00101 Helsinki, Finland
| | - Reija Ruuhela
- Finnish Meteorological Institute, Meteorological and Marine Research Programme, Weather and Climate Change Impact Research, P.O. Box 503, 00101 Helsinki, Finland
| | - Kari Auranen
- The Center of Statistics, University of Turku, 20500 Turku, Finland
| | - Kaisa Lakkala
- Finnish Meteorological Institute, Space and Earth Observation Centre, Earth Observation Research, P.O. Box 503, 00101 Helsinki, Finland
- Finnish Meteorological Institute, Climate Research Programme, Atmospheric Research Center of Eastern Finland, P.O. Box 503, 00101 Helsinki, Finland
| | - Anu Heikkilä
- Finnish Meteorological Institute, Climate Research Programme, Atmospheric Research Center of Eastern Finland, P.O. Box 503, 00101 Helsinki, Finland
| | - Hilppa Gregow
- Finnish Meteorological Institute, Meteorological and Marine Research Programme, Weather and Climate Change Impact Research, P.O. Box 503, 00101 Helsinki, Finland
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Ford JD, Zavaleta-Cortijo C, Ainembabazi T, Anza-Ramirez C, Arotoma-Rojas I, Bezerra J, Chicmana-Zapata V, Galappaththi EK, Hangula M, Kazaana C, Lwasa S, Namanya D, Nkwinti N, Nuwagira R, Okware S, Osipova M, Pickering K, Singh C, Berrang-Ford L, Hyams K, Miranda JJ, Naylor A, New M, van Bavel B. Interactions between climate and COVID-19. Lancet Planet Health 2022; 6:e825-e833. [PMID: 36208645 PMCID: PMC9534524 DOI: 10.1016/s2542-5196(22)00174-7] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 06/25/2022] [Accepted: 07/12/2022] [Indexed: 05/22/2023]
Abstract
In this Personal View, we explain the ways that climatic risks affect the transmission, perception, response, and lived experience of COVID-19. First, temperature, wind, and humidity influence the transmission of COVID-19 in ways not fully understood, although non-climatic factors appear more important than climatic factors in explaining disease transmission. Second, climatic extremes coinciding with COVID-19 have affected disease exposure, increased susceptibility of people to COVID-19, compromised emergency responses, and reduced health system resilience to multiple stresses. Third, long-term climate change and prepandemic vulnerabilities have increased COVID-19 risk for some populations (eg, marginalised communities). The ways climate and COVID-19 interact vary considerably between and within populations and regions, and are affected by dynamic and complex interactions with underlying socioeconomic, political, demographic, and cultural conditions. These conditions can lead to vulnerability, resilience, transformation, or collapse of health systems, communities, and livelihoods throughout varying timescales. It is important that COVID-19 response and recovery measures consider climatic risks, particularly in locations that are susceptible to climate extremes, through integrated planning that includes public health, disaster preparedness, emergency management, sustainable development, and humanitarian response.
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Affiliation(s)
- James D Ford
- Priestley International Centre for Climate, University of Leeds, Leeds, UK.
| | - Carol Zavaleta-Cortijo
- Intercultural Citizenship and Indigenous Health Unit, Cayetano Heredia University, Lima, Peru
| | - Triphini Ainembabazi
- Department of Geography, Geo-Informatics, and Climatic Sciences, Makerere University, Kampala, Uganda
| | - Cecilia Anza-Ramirez
- Center of Excellence in Chronic Diseases, Universidad Peruana Cayetano Heredia, Lima, Peru
| | | | - Joana Bezerra
- Community Engagement, Rhodes University, Makhanda, South Africa
| | | | | | - Martha Hangula
- Department of Livestock Production, Agribusiness, and Economics, University of Namibia, Oshakati, Namibia
| | | | - Shuaib Lwasa
- Department of Geography, Geo-Informatics, and Climatic Sciences, Makerere University, Kampala, Uganda
| | | | - Nosipho Nkwinti
- Community Engagement, Rhodes University, Makhanda, South Africa
| | | | - Samuel Okware
- Uganda National Health Research Organisation, Entebbe, Uganda
| | - Maria Osipova
- Arctic State Institute of Culture and Arts, North-Eastern Federal University, Yakutsk, Russia
| | - Kerrie Pickering
- Sustainability Research Centre, University of the Sunshine Coast, Buderim, QLD, Australia
| | - Chandni Singh
- School of Environment and Sustainability, Indian Institute for Human Settlements, Bangalore, India
| | - Lea Berrang-Ford
- Priestley International Centre for Climate, University of Leeds, Leeds, UK
| | - Keith Hyams
- Department of Politics and International Studies, University of Warwick, Coventry, UK
| | - J Jaime Miranda
- Center of Excellence in Chronic Diseases, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Angus Naylor
- School of Public Health and Social Policy, University of Victoria, Victoria, BC, Canada
| | - Mark New
- Environmental and Geographical Science, University of Cape Town, Cape Town, South Africa
| | - Bianca van Bavel
- Priestley International Centre for Climate, University of Leeds, Leeds, UK
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31
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Scabbia G, Sanfilippo A, Mazzoni A, Bachour D, Perez-Astudillo D, Bermudez V, Wey E, Marchand-Lasserre M, Saboret L. Does climate help modeling COVID-19 risk and to what extent? PLoS One 2022; 17:e0273078. [PMID: 36070304 PMCID: PMC9451080 DOI: 10.1371/journal.pone.0273078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Accepted: 08/01/2022] [Indexed: 11/25/2022] Open
Abstract
A growing number of studies suggest that climate may impact the spread of COVID-19. This hypothesis is supported by data from similar viral contagions, such as SARS and the 1918 Flu Pandemic, and corroborated by US influenza data. However, the extent to which climate may affect COVID-19 transmission rates and help modeling COVID-19 risk is still not well understood. This study demonstrates that such an understanding is attainable through the development of regression models that verify how climate contributes to modeling COVID-19 transmission, and the use of feature importance techniques that assess the relative weight of meteorological variables compared to epidemiological, socioeconomic, environmental, and global health factors. The ensuing results show that meteorological factors play a key role in regression models of COVID-19 risk, with ultraviolet radiation (UV) as the main driver. These results are corroborated by statistical correlation analyses and a panel data fixed-effect model confirming that UV radiation coefficients are significantly negatively correlated with COVID-19 transmission rates.
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Affiliation(s)
- Giovanni Scabbia
- Qatar Environment and Energy Research Institute, Hamad Bin Khalifa University – Qatar Foundation, Doha, Qatar
| | - Antonio Sanfilippo
- Qatar Environment and Energy Research Institute, Hamad Bin Khalifa University – Qatar Foundation, Doha, Qatar
| | - Annamaria Mazzoni
- Qatar Environment and Energy Research Institute, Hamad Bin Khalifa University – Qatar Foundation, Doha, Qatar
| | - Dunia Bachour
- Qatar Environment and Energy Research Institute, Hamad Bin Khalifa University – Qatar Foundation, Doha, Qatar
| | - Daniel Perez-Astudillo
- Qatar Environment and Energy Research Institute, Hamad Bin Khalifa University – Qatar Foundation, Doha, Qatar
| | - Veronica Bermudez
- Qatar Environment and Energy Research Institute, Hamad Bin Khalifa University – Qatar Foundation, Doha, Qatar
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32
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Menon NG, Mohapatra S. The COVID-19 pandemic: Virus transmission and risk assessment. CURRENT OPINION IN ENVIRONMENTAL SCIENCE & HEALTH 2022; 28:100373. [PMID: 35669052 PMCID: PMC9156429 DOI: 10.1016/j.coesh.2022.100373] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
The coronaviruses are the largest known RNA viruses of which SASR-CoV-2 has been spreading continuously due to its repeated mutation triggered by several environmental factors. Multiple human interventions and lessons learned from the SARS 2002 outbreak helped reduce its spread considerably, and thus, the virus was contained but the emerging mutations burdened the medical facility leading to many deaths in the world. As per the world health organization (WHO) droplet mode transmission is the most common mode of SASR-CoV-2 transmission to which environmental factors including temperature and humidity play a major role. This article highlights the responsibility of environmental causes that would affect the distribution and fate of the virus. Recent development in the risk assessment models is also covered in this article.
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Affiliation(s)
- N Gayathri Menon
- Centre for Research in Nanotechnology and Science (CRNTS), Indian Institute of Technology Bombay, India
| | - Sanjeeb Mohapatra
- NUS Environmental Research Institute, National University of Singapore, 1 Create Way, Create Tower, #15-02, Singapore 138602, Singapore
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33
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Cao Y, Whittington JD, Kausrud K, Li R, Stenseth NC. The Relative Contribution of Climatic, Demographic Factors, Disease Control Measures and Spatiotemporal Heterogeneity to Variation of Global COVID-19 Transmission. GEOHEALTH 2022; 6:e2022GH000589. [PMID: 35946036 PMCID: PMC9349723 DOI: 10.1029/2022gh000589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 06/24/2022] [Accepted: 06/30/2022] [Indexed: 06/15/2023]
Abstract
Despite a substantial number of COVID-19 related research papers published, it remains unclear as to which factors are associated with the observed variation in global transmission and what are their relative levels of importance. This study applies a rigorous statistical framework to provide robust estimations of the factor effects for a global and integrated perspective on this issue. We developed a mixed effect model exploring the relative importance of potential factors driving COVID-19 transmission while incorporating spatial and temporal heterogeneity of spread. We use an integrated data set for 87 countries across six continents for model specification and fitting. The best model accounts for 70.4% of the variance in the data analyzed: 10 fixed effect factors explain 20.5% of the variance, random temporal and spatial effects account for 50% of the variance. The fixed effect factors are classified into climatic, demographic and disease control groups. The explained variance in global transmission by the three groups are 0.6%, 1.1%, and 4.4% respectively. The high proportion of variance accounted for by random effects indicated striking differences in temporal transmission trajectories and effects of population mobility among the countries. In particular, the country-specific mobility-transmission relationship turns out to be the most important factor in explaining the observed global variation of transmission in the early phase of COVID-19 pandemic.
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Affiliation(s)
- Yihan Cao
- Centre for Ecological and Evolutionary Synthesis (CEES)Department of BiosciencesUniversity of OsloOsloNorway
| | - Jason D. Whittington
- Centre for Ecological and Evolutionary Synthesis (CEES)Department of BiosciencesUniversity of OsloOsloNorway
| | | | - Ruiyun Li
- Centre for Ecological and Evolutionary Synthesis (CEES)Department of BiosciencesUniversity of OsloOsloNorway
| | - Nils Chr. Stenseth
- Centre for Ecological and Evolutionary Synthesis (CEES)Department of BiosciencesUniversity of OsloOsloNorway
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34
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Wei Y, Dong Z, Fan W, Xu K, Tang S, Wang Y, Wu F. A narrative review on the role of temperature and humidity in COVID-19: Transmission, persistence, and epidemiological evidence. ECO-ENVIRONMENT & HEALTH (ONLINE) 2022; 1:73-85. [PMID: 38013745 PMCID: PMC9181277 DOI: 10.1016/j.eehl.2022.04.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 03/30/2022] [Accepted: 04/28/2022] [Indexed: 12/11/2022]
Abstract
Since December 2019, the 2019 coronavirus disease (COVID-19) outbreak has become a global pandemic. Understanding the role of environmental conditions is important in impeding the spread of COVID-19. Given that airborne spread and contact transmission are considered the main pathways for the spread of COVID-19, this narrative review first summarized the role of temperature and humidity in the airborne trajectory of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Meanwhile, we reviewed the persistence of the virus in aerosols and on inert surfaces and summarized how the persistence of SARS-CoV-2 is affected by temperature and humidity. We also examined the existing epidemiological evidence and addressed the limitations of these epidemiological studies. Although uncertainty remains, more evidence may support the idea that high temperature is slightly and negatively associated with COVID-19 growth, while the conclusion for humidity is still conflicting. Nonetheless, the spread of COVID-19 appears to have been controlled primarily by government interventions rather than environmental factors.
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Affiliation(s)
- Yuan Wei
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Zhaomin Dong
- School of Space and Environment, Beihang University, Beijing 102206, China
| | - Wenhong Fan
- School of Space and Environment, Beihang University, Beijing 102206, China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing 100083, China
| | - Kaiqiang Xu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Song Tang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Ying Wang
- School of Space and Environment, Beihang University, Beijing 102206, China
| | - Fengchang Wu
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
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35
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De Cos Guerra O, Castillo Salcines V, Cantarero Prieto D. Are spatial patterns of Covid-19 changing? Spatiotemporal analysis over four waves in the region of Cantabria, Spain. TRANSACTIONS IN GIS : TG 2022; 26:1981-2003. [PMID: 35601792 PMCID: PMC9115338 DOI: 10.1111/tgis.12919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
This research approaches the empirical study of the pandemic from a social science perspective. The main goal is to reveal spatiotemporal changes in Covid-19, at regional scale, using GIS technologies and the emerging three-dimensional bins method. We analyze a case study of the region of Cantabria (northern Spain) based on 29,288 geocoded positive Covid-19 cases in the four waves from the outset in March 2020 to June 2021. Our results suggest three main spatial processes: a reversal in the spatial trend, spreading first followed by contraction in the third and fourth waves; then the reduction of hot spots that represent problematic areas because of high presence of cases and growing trends; and finally, an increase in cold spots. All this generates relevant knowledge to help policy-makers from regional governments to design efficient containment and mitigation strategies. Our research is conducted from a geoprevention perspective, based on the application of targeted measures depending on spatial patterns of Covid-19 in real time. It represents an opportunity to reduce the socioeconomic impact of global containment measures in pandemic management.
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Affiliation(s)
- Olga De Cos Guerra
- Department of Geography, Urban and Regional PlanningUniversidad de CantabriaSantanderSpain
- Research Group on Health Economics and Health Services Management—Marqués de Valdecilla Research Institute (IDIVAL)SantanderSpain
| | - Valentín Castillo Salcines
- Department of Geography, Urban and Regional PlanningUniversidad de CantabriaSantanderSpain
- Research Group on Health Economics and Health Services Management—Marqués de Valdecilla Research Institute (IDIVAL)SantanderSpain
| | - David Cantarero Prieto
- Research Group on Health Economics and Health Services Management—Marqués de Valdecilla Research Institute (IDIVAL)SantanderSpain
- Department of EconomicsUniversidad de CantabriaSantanderSpain
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36
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Rahmandad H, Xu R, Ghaffarzadegan N. Enhancing long-term forecasting: Learning from COVID-19 models. PLoS Comput Biol 2022; 18:e1010100. [PMID: 35587466 PMCID: PMC9119494 DOI: 10.1371/journal.pcbi.1010100] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 04/12/2022] [Indexed: 12/11/2022] Open
Abstract
While much effort has gone into building predictive models of the COVID-19 pandemic, some have argued that early exponential growth combined with the stochastic nature of epidemics make the long-term prediction of contagion trajectories impossible. We conduct two complementary studies to assess model features supporting better long-term predictions. First, we leverage the diverse models contributing to the CDC repository of COVID-19 USA death projections to identify factors associated with prediction accuracy across different projection horizons. We find that better long-term predictions correlate with: (1) capturing the physics of transmission (instead of using black-box models); (2) projecting human behavioral reactions to an evolving pandemic; and (3) resetting state variables to account for randomness not captured in the model before starting projection. Second, we introduce a very simple model, SEIRb, that incorporates these features, and few other nuances, offers informative predictions for as far as 20-weeks ahead, with accuracy comparable with the best models in the CDC set. Key to the long-term predictive power of multi-wave COVID-19 trajectories is capturing behavioral responses endogenously: balancing feedbacks where the perceived risk of death continuously changes transmission rates through the adoption and relaxation of various Non-Pharmaceutical Interventions (NPIs). Long-term projections of COVID-19 trajectory have been used to inform various policies and decisions such as planning intensive care capacity, selecting clinical trial locations, and deciding on economic policy packages. However, these types of long-term forecasts are challenging as epidemics are complex: they include reinforcing contagion mechanisms that create exponential growth, are moderated by randomness in environmental and social determinants of transmission, and are subject to endogenous human responses to evolving risk perceptions. In this study we take a step towards systematically examining the modeling choices that regulate COVID-19 forecasting accuracy in two complementary studies. First, we leverage the diverse models contributing to the CDC repository of COVID-19 USA death projections to identify factors associated with prediction accuracy across different projection horizons. Second, we design a very simple forecasting model that only incorporates the key features identified in the first study, and show that the long-term prediction accuracy of this model is comparable with the best models in the CDC set. We conclude that forecasting models responding to future epidemics would benefit from starting small: first incorporating key mechanistic features, important behavioral feedbacks, and simple state-resetting approaches and then expanding to capture other features. Our study shows that the key to the long-term predictive power of epidemic models is an endogenous representation of human behavior in interaction with the evolving epidemic.
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Affiliation(s)
- Hazhir Rahmandad
- Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Ran Xu
- Department of Allied Health Sciences, University of Connecticut, Storrs, Connecticut, United States of America
| | - Navid Ghaffarzadegan
- Department of Industrial and Systems Engineering, Virginia Tech, Falls Church, Virginia, United States of America
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37
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Ebert K, Houts R, Noce S. Lower COVID-19 Incidence in Low-Continentality West-Coast Areas of Europe. GEOHEALTH 2022; 6:e2021GH000568. [PMID: 35516911 PMCID: PMC9066745 DOI: 10.1029/2021gh000568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Revised: 04/08/2022] [Accepted: 04/11/2022] [Indexed: 06/14/2023]
Abstract
In March 2020, the first known cases of COVID-19 occurred in Europe. Subsequently, the pandemic developed a seasonal pattern. The incidence of COVID-19 comprises spatial heterogeneity and seasonal variations, with lower and/or shorter peaks resulting in lower total incidence and higher and/or longer peaks resulting higher total incidence. The reason behind this phenomena is still unclear. Unraveling factors that explain why certain places have higher versus lower total COVID-19 incidence can help health decision makers understand and plan for future waves of the pandemic. We test whether differences in the total incidence of COVID-19 within five European countries (Norway, Sweden, Germany, Italy, and Spain), correlate with two environmental factors: the Köppen-Geiger climate zones and the Continentality Index, while statistically controlling for crowding. Our results show that during the first 16 months of the pandemic (March 2020 to July 2021), climate zones with larger annual differences in temperature and annually distributed precipitation show a higher total incidence than climate zones with smaller differences in temperature and dry seasons. This coincides with lower continentality values. Total incidence increases with continentality, up to a Continentality Index value of 19, where a peak is reached in the semicontinental zone. Low continentality (high oceanic influence) appears to be a strong suppressing factor for COVID-19 spread. The incidence in our study area is lowest at open low continentality west coast areas.
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Affiliation(s)
- Karin Ebert
- Natural Sciences, Technology and Environmental StudiesSödertörn UniversityStockholmSweden
| | - Renate Houts
- Department of Psychology and NeuroscienceDuke UniversityDurhamNCUSA
| | - Sergio Noce
- Fondazione Centro Euro‐Mediterraneo sui Cambiamenti Climatici (CMCC)Division on Impacts on Agriculture, Forests and Ecosystem Services (IAFES)ViterboItaly
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38
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Cappi R, Casini L, Tosi D, Roccetti M. Questioning the seasonality of SARS-COV-2: a Fourier spectral analysis. BMJ Open 2022; 12:e061602. [PMID: 35443965 PMCID: PMC9021461 DOI: 10.1136/bmjopen-2022-061602] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 04/04/2022] [Indexed: 12/24/2022] Open
Abstract
OBJECTIVES To investigate the hypothesis of a seasonal periodicity, driven by climate, in the contagion resurgence of COVID-19 in the period February 2020-December 2021. DESIGN An observational study of 30 countries from different geographies and climates. For each country, a Fourier spectral analysis was performed with the series of the daily SARS-CoV-2 infections, looking for peaks in the frequency spectrum that could correspond to a recurrent cycle of a given length. SETTINGS Public data of the daily SARS-CoV-2 infections from 30 different countries and five continents. PARTICIPANTS Only publicly available data were utilised for this study, patients and/or the public were not involved in any phase of this study. RESULTS All the 30 investigated countries have seen the recurrence of at least one COVID-19 wave, repeating over a period in the range 3-9 months, with a peak of magnitude at least half as large as that of the highest peak ever experienced since the beginning of the pandemic until December 2021. The distance in days between the two highest peaks in each country was computed and then averaged over the 30 countries, yielding a mean of 190 days (SD 100). This suggests that recurrent outbreaks may repeat with cycles of different lengths, without a precisely predictable seasonality of 1 year. CONCLUSION Our findings suggest that COVID-19 outbreaks are likely to occur worldwide, with cycles of repetition of variable lengths. The Fourier analysis of 30 different countries has not found evidence in favour of a seasonality that recurs over 1year period, solely or with a precisely fixed periodicity.
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Affiliation(s)
- Riccardo Cappi
- Department of Theoretical and Applied Sciences, University of Insubria, Varese, Lombardia, Italy
| | - Luca Casini
- Department of Computer Science and Engineering, University of Bologna, Bologna, Emilia-Romagna, Italy
| | - Davide Tosi
- Department of Theoretical and Applied Sciences, University of Insubria, Varese, Lombardia, Italy
| | - Marco Roccetti
- Department of Computer Science and Engineering, University of Bologna, Bologna, Emilia-Romagna, Italy
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39
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Santurtún A, Colom ML, Fdez-Arroyabe P, Real ÁD, Fernández-Olmo I, Zarrabeitia MT. Exposure to particulate matter: Direct and indirect role in the COVID-19 pandemic. ENVIRONMENTAL RESEARCH 2022; 206:112261. [PMID: 34687752 PMCID: PMC8527737 DOI: 10.1016/j.envres.2021.112261] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2021] [Revised: 10/19/2021] [Accepted: 10/19/2021] [Indexed: 05/16/2023]
Abstract
Knowing the transmission factors and the natural environment that favor the spread of a viral infection is crucial to stop outbreaks and develop effective preventive strategies. This work aims to evaluate the role of Particulate Matter (PM) in the COVID-19 pandemic, focusing especially on that of PM as a vector for SARS-CoV-2. Exposure to PM has been related to new cases and to the clinical severity of people infected by SARS-CoV-2, which can be explained by the oxidative stress and the inflammatory response generated by these particles when entering the respiratory system, as well as by the role of PM in the expression of ACE-2 in respiratory cells in human hosts. In addition, different authors have detected SARS-CoV-2 RNA in PM sampled both in outdoor and indoor environments. The results of various studies lead to the hypothesis that the aerosols emitted by an infected person could be deposited in other suspended particles, sometimes of natural but especially of anthropogenic origin, that form the basal PM. However, the viability of the virus in PM has not yet been demonstrated. Should PM be confirmed as a vector of transmission, prevention strategies ought to be adapted, and PM sampling in outdoor environments could become an indicator of viral load in a specific area.
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Affiliation(s)
- Ana Santurtún
- Legal Medicine and Toxicology Area, Department of Physiology and Pharmacology. Faculty of Medicine. University of Cantabria, Santander, Spain.
| | - Marina L Colom
- Legal Medicine and Toxicology Area, Department of Physiology and Pharmacology. Faculty of Medicine. University of Cantabria, Santander, Spain
| | - Pablo Fdez-Arroyabe
- Geography and Planning Department, Geobiomet Research Group. University of Cantabria, Santander, Spain
| | - Álvaro Del Real
- Medicine and Psychiatry Department. University of Cantabria, Santander, Spain
| | - Ignacio Fernández-Olmo
- Chemical and Molecular Engineering Department. University of Cantabria, Santander, Spain
| | - María T Zarrabeitia
- Legal Medicine and Toxicology Area, Department of Physiology and Pharmacology. Faculty of Medicine. University of Cantabria, Santander, Spain
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40
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Rovetta A, Bhagavathula AS. The Impact of COVID-19 on Mortality in Italy: Retrospective Analysis of Epidemiological Trends. JMIR Public Health Surveill 2022; 8:e36022. [PMID: 35238784 PMCID: PMC8993143 DOI: 10.2196/36022] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 01/31/2022] [Accepted: 03/03/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Despite the available evidence on its severity, COVID-19 has often been compared with seasonal flu by some conspirators and even scientists. Various public discussions arose about the noncausal correlation between COVID-19 and the observed deaths during the pandemic period in Italy. OBJECTIVE This paper aimed to search for endogenous reasons for the mortality increase recorded in Italy during 2020 to test this controversial hypothesis. Furthermore, we provide a framework for epidemiological analyses of time series. METHODS We analyzed deaths by age, sex, region, and cause of death in Italy from 2011 to 2019. Ordinary least squares (OLS) linear regression analyses and autoregressive integrated moving average (ARIMA) were used to predict the best value for 2020. A Grubbs 1-sided test was used to assess the significance of the difference between predicted and observed 2020 deaths/mortality. Finally, a 1-sample t test was used to compare the population of regional excess deaths to a null mean. The relationship between mortality and predictive variables was assessed using OLS multiple regression models. Since there is no uniform opinion on multicomparison adjustment and false negatives imply great epidemiological risk, the less-conservative Siegel approach and more-conservative Holm-Bonferroni approach were employed. By doing so, we provided the reader with the means to carry out an independent analysis. RESULTS Both ARIMA and OLS linear regression models predicted the number of deaths in Italy during 2020 to be between 640,000 and 660,000 (range of 95% CIs: 620,000-695,000) against the observed value of above 750,000. We found strong evidence supporting that the death increase in all regions (average excess=12.2%) was not due to chance (t21=7.2; adjusted P<.001). Male and female national mortality excesses were 18.4% (P<.001; adjusted P=.006) and 14.1% (P=.005; adjusted P=.12), respectively. However, we found limited significance when comparing male and female mortality residuals' using the Mann-Whitney U test (P=.27; adjusted P=.99). Finally, mortality was strongly and positively correlated with latitude (R=0.82; adjusted P<.001). In this regard, the significance of the mortality increases during 2020 varied greatly from region to region. Lombardy recorded the highest mortality increase (38% for men, adjusted P<.001; 31% for women, P<.001; adjusted P=.006). CONCLUSIONS Our findings support the absence of historical endogenous reasons capable of justifying the mortality increase observed in Italy during 2020. Together with the current knowledge on SARS-CoV-2, these results provide decisive evidence on the devastating impact of COVID-19. We suggest that this research be leveraged by government, health, and information authorities to furnish proof against conspiracy hypotheses that minimize COVID-19-related risks. Finally, given the marked concordance between ARIMA and OLS regression, we suggest that these models be exploited for public health surveillance. Specifically, meaningful information can be deduced by comparing predicted and observed epidemiological trends.
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Affiliation(s)
| | - Akshaya Srikanth Bhagavathula
- Institute of Public Health, College of Medicine and Health Sciences, United Arab Emirates University, Abu Dhabi, United Arab Emirates
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Weaver AK, Head JR, Gould CF, Carlton EJ, Remais JV. Environmental Factors Influencing COVID-19 Incidence and Severity. Annu Rev Public Health 2022; 43:271-291. [PMID: 34982587 PMCID: PMC10044492 DOI: 10.1146/annurev-publhealth-052120-101420] [Citation(s) in RCA: 59] [Impact Index Per Article: 29.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Emerging evidence supports a link between environmental factors-including air pollution and chemical exposures, climate, and the built environment-and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission and coronavirus disease 2019 (COVID-19) susceptibility and severity. Climate, air pollution, and the built environment have long been recognized to influence viral respiratory infections, and studies have established similar associations with COVID-19 outcomes. More limited evidence links chemical exposures to COVID-19. Environmental factors were found to influence COVID-19 through four major interlinking mechanisms: increased risk of preexisting conditions associated with disease severity; immune system impairment; viral survival and transport; and behaviors that increase viral exposure. Both data and methodologic issues complicate the investigation of these relationships, including reliance on coarse COVID-19 surveillance data; gaps in mechanistic studies; and the predominance of ecological designs. We evaluate the strength of evidence for environment-COVID-19 relationships and discuss environmental actions that might simultaneously address the COVID-19 pandemic, environmental determinants of health, and health disparities.
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Affiliation(s)
- Amanda K Weaver
- Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, Berkeley, California, USA; ,
| | - Jennifer R Head
- Department of Epidemiology, School of Public Health, University of California, Berkeley, Berkeley, California, USA;
| | - Carlos F Gould
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, USA;
- Department of Earth System Science, Stanford University, Stanford, California, USA
| | - Elizabeth J Carlton
- Department of Environmental and Occupational Health, Colorado School of Public Health, University of Colorado, Anschutz, Aurora, Colorado, USA;
| | - Justin V Remais
- Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, Berkeley, California, USA; ,
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Ganti K, Ferreri LM, Lee CY, Bair CR, Delima GK, Holmes KE, Suthar MS, Lowen AC. Timing of exposure is critical in a highly sensitive model of SARS-CoV-2 transmission. PLoS Pathog 2022; 18:e1010181. [PMID: 35333914 PMCID: PMC8986102 DOI: 10.1371/journal.ppat.1010181] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 04/06/2022] [Accepted: 03/09/2022] [Indexed: 01/19/2023] Open
Abstract
Transmission efficiency is a critical factor determining the size of an outbreak of infectious disease. Indeed, the propensity of SARS-CoV-2 to transmit among humans precipitated and continues to sustain the COVID-19 pandemic. Nevertheless, the number of new cases among contacts is highly variable and underlying reasons for wide-ranging transmission outcomes remain unclear. Here, we evaluated viral spread in golden Syrian hamsters to define the impact of temporal and environmental conditions on the efficiency of SARS-CoV-2 transmission through the air. Our data show that exposure periods as brief as one hour are sufficient to support robust transmission. However, the timing after infection is critical for transmission success, with the highest frequency of transmission to contacts occurring at times of peak viral load in the donor animals. Relative humidity and temperature had no detectable impact on transmission when exposures were carried out with optimal timing and high inoculation dose. However, contrary to expectation, trends observed with sub-optimal exposure timing and lower inoculation dose suggest improved transmission at high relative humidity or high temperature. In sum, among the conditions tested, our data reveal the timing of exposure to be the strongest determinant of SARS-CoV-2 transmission success and implicate viral load as an important driver of transmission.
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Affiliation(s)
- Ketaki Ganti
- Department of Microbiology and Immunology, Emory University School of Medicine, Atlanta, Georgia, United States of America
| | - Lucas M. Ferreri
- Department of Microbiology and Immunology, Emory University School of Medicine, Atlanta, Georgia, United States of America
| | - Chung-Young Lee
- Department of Microbiology and Immunology, Emory University School of Medicine, Atlanta, Georgia, United States of America
| | - Camden R. Bair
- Department of Microbiology and Immunology, Emory University School of Medicine, Atlanta, Georgia, United States of America
| | - Gabrielle K. Delima
- Department of Microbiology and Immunology, Emory University School of Medicine, Atlanta, Georgia, United States of America
| | - Kate E. Holmes
- Department of Microbiology and Immunology, Emory University School of Medicine, Atlanta, Georgia, United States of America
| | - Mehul S. Suthar
- Department of Microbiology and Immunology, Emory University School of Medicine, Atlanta, Georgia, United States of America
- Emory Vaccine Center, Emory University School of Medicine, Atlanta, Georgia, United States of America
- Center for Childhood Infections and Vaccines of Children’s Healthcare of Atlanta, Department of Pediatrics, Emory University School of Medicine, Atlanta, Georgia, United States of America
- Emory-UGA Center of Excellence for Influenza Research and Surveillance [CEIRS], Atlanta, Georgia, United States of America
| | - Anice C. Lowen
- Department of Microbiology and Immunology, Emory University School of Medicine, Atlanta, Georgia, United States of America
- Emory-UGA Center of Excellence for Influenza Research and Surveillance [CEIRS], Atlanta, Georgia, United States of America
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The effects of air pollution, meteorological parameters, and climate change on COVID-19 comorbidity and health disparities: A systematic review. ENVIRONMENTAL CHEMISTRY AND ECOTOXICOLOGY 2022; 4. [PMCID: PMC9568272 DOI: 10.1016/j.enceco.2022.10.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Air pollutants, especially particulate matter, and other meteorological factors serve as important carriers of infectious microbes and play a critical role in the spread of disease. However, there remains uncertainty about the relationship among particulate matter, other air pollutants, meteorological conditions and climate change and the spread of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), hereafter referred to as COVID-19. A systematic review was conducted using PRISMA guidelines to identify the relationship between air quality, meteorological conditions and climate change, and COVID-19 risk and outcomes, host related factors, co-morbidities and disparities. Out of a total of 170,296 scientific publications screened, 63 studies were identified that focused on the relationship between air pollutants and COVID-19. Additionally, the contribution of host related-factors, co-morbidities, and health disparities was discussed. This review found a preponderance of evidence of a positive relationship between PM2.5, other air pollutants, and meteorological conditions and climate change on COVID-19 risk and outcomes. The effects of PM2.5, air pollutants, and meteorological conditions on COVID-19 mortalities were most commonly experienced by socially disadvantaged and vulnerable populations. Results however, were not entirely consistent, and varied by geographic region and study. Opportunities for using data to guide local response to COVID-19 are identified.
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The impact of temperature on the transmissibility potential and virulence of COVID-19 in Tokyo, Japan. Sci Rep 2021; 11:24477. [PMID: 34966171 PMCID: PMC8716537 DOI: 10.1038/s41598-021-04242-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 12/17/2021] [Indexed: 11/22/2022] Open
Abstract
Assessing the impact of temperature on COVID-19 epidemiology is critical for implementing non-pharmaceutical interventions. However, few studies have accounted for the nature of contagious diseases, i.e., their dependent happenings. We aimed to quantify the impact of temperature on the transmissibility and virulence of COVID-19 in Tokyo, Japan, employing two epidemiological measurements of transmissibility and severity: the effective reproduction number (\documentclass[12pt]{minimal}
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\begin{document}$$R_{t}$$\end{document}Rt) and case fatality risk (CFR). We estimated the \documentclass[12pt]{minimal}
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\begin{document}$$R_{t}$$\end{document}Rt and time-delay adjusted CFR and to subsequently assess the nonlinear and delayed effect of temperature on \documentclass[12pt]{minimal}
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\begin{document}$$R_{t}$$\end{document}Rt and time-delay adjusted CFR. For \documentclass[12pt]{minimal}
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\begin{document}$$R_{t}$$\end{document}Rt at low temperatures, the cumulative relative risk (RR) at the first temperature percentile (3.3 °C) was 1.3 (95% confidence interval (CI): 1.1–1.7). As for the virulence to humans, moderate cold temperatures were associated with higher CFR, and CFR also increased as the temperature rose. The cumulative RR at the 10th and 99th percentiles of temperature (5.8 °C and 30.8 °C) for CFR were 3.5 (95% CI: 1.3–10.0) and 6.4 (95% CI: 4.1–10.1). Our results suggest the importance to take precautions to avoid infection in both cold and warm seasons to avoid severe cases of COVID-19. The results and our proposed approach will also help in assessing the possible seasonal course of COVID-19 in the future.
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Tapia-Muñoz T, González-Santa Cruz A, Clarke H, Morris W, Palmeiro-Silva Y, Allel K. COVID-19 attributed mortality and ambient temperature: a global ecological study using a two-stage regression model. Pathog Glob Health 2021; 116:319-329. [PMID: 34842049 PMCID: PMC9248943 DOI: 10.1080/20477724.2021.2007336] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
A negative correlation between ambient temperature and COVID-19 mortality has been observed. However, the World Meteorological Organization (WMO) has reinforced the importance of government interventions and warned countries against relaxing control measures due to warmer temperatures. Further understanding of this relationship is needed to help plan vaccination campaigns opportunely. Using a two-stage regression model, we conducted cross-sectional and longitudinal analyses to evaluate the association between monthly ambient temperature lagged by one month with the COVID-19 number of deaths and the probability of high-level of COVID-19 mortality in 150 countries during time t = 60, 90, and 120 days since the onset. First, we computed a log-linear regression to predict the pre-COVID-19 respiratory disease mortality to homogenize the baseline disease burden within countries. Second, we employed negative binomial and logistic regressions to analyze the linkage between the ambient temperature and our outcomes, adjusting by pre-COVID-19 respiratory disease mortality rate, among other factors. The increase of one Celsius degree in ambient temperature decreases the incidence of COVID-19 deaths (IRR = 0.93; SE: 0.026, p-value<0.001) and the probability of high-level COVID-19 mortality (OR = 0.96; SE: 0.019; p-value<0.001) over time. High-income countries from the northern hemisphere had lower temperatures and were most affected by pre-COVID respiratory disease mortality and COVID-19 mortality. This study provides a global perspective corroborating the negative association between COVID-19 mortality and ambient temperature. Our longitudinal findings support the statement made by the WMO. Effective, opportune, and sustained reaction from countries can help capitalize on higher temperatures’ protective role including the timely rollout of vaccination campaigns.
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Affiliation(s)
- Thamara Tapia-Muñoz
- Department of Behavioural Science and Health, Institute of Epidemiology and Health Care, University College London, London, UK
| | | | - Harrison Clarke
- Institute for Global Health, University College London, London, UK
| | - Walter Morris
- Institute for Global Health, University College London, London, UK
| | | | - Kasim Allel
- Institute for Global Health, University College London, London, UK
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